Papers

International aid is usually described as flows between countries, hiding the organisations money passes through. We reconstruct global aid's organisational network from 10.9 million International Aid Transparency Initiative records, tracing over one million payments worth about US\$1 trillion between 22,773 organisations. First, centrality, measured as how far a funder's money can travel downstream, is not size. Relative to their number of partners, giant bilateral donors (USAID, UK FCDO) are only as central as expected, while the most central positions belong to foundations (Gates, Hewlett), research organisations (ODI, University of Oxford) and delivery contractors (Mott MacDonald). Second, principal component analysis of eleven flow measures separates organisations along three lines: whether they mainly give or receive, how much money flows in, and how reliant they are on few partners. Humanitarian organisations emerge from the flows alone, tending to handle larger inflows. Third, almost all organisations form one connected network, yet money moves down long, one-way chains. Among five comparison networks, aid most resembles international banking, with a few large hubs feeding many small players, but has far fewer loops and is more strictly top-down. This suggests a system with little redundancy, where losing a major funder or intermediary could cut off organisations downstream, a pressing question after recent donor cuts.

Wave-physics-based intelligent sensing has driven multidisciplinary applications from smart industries to decision-making systems. Traditional sensing paradigms transform physical waveforms into human-understandable intermediate representations through preprocessing. Such transformations inherently cause information loss owing to data processing inequality (DPI). Here, we established a theoretical framework for physical computing at the DPI upper limit. Physical computing avoids information loss during preprocessing by directly extracting information from physical waveforms, achieving the theoretical maximum of accessible information as determined by the DPI. Furthermore, physical computing comprehensively utilizes multiple dimensions of physical waveforms, thereby enhancing the upper limit of information capture capability. Electromagnetic sensing experiments have demonstrated that physical computing can achieve 100% sensing accuracy, substantially outperforming traditional sensing paradigms. The proposed theoretical framework of physical computing offers a promising path towards enhancing the information-capture capability of next-generation intelligent sensing systems.

Hadronic physics has gradually emerged as one of the growing research frontiers in Indonesia, driven by efforts to better understand the properties of the strong interaction and the internal structure of hadrons from the fundamental principles of Quantum Chromodynamics. In the last few decades, Indonesian researchers have made significant contributions to developing various theoretical and phenomenological aspects of hadrons. In addition, on the experimental side, Indonesian scientists have participated in hadron experiment facilities overseas, such as the ALICE collaboration at CERN, which has strengthened the scientific activities and networks and has supported the training of young Indonesian researchers. In the present paper, we review Indonesian scientists' contributions to hadronic physics, highlight ongoing research directions in both experimental and theoretical, and outline strategies for future development toward integration into the international hadronic physics community.

We propose a compact scheme based on ultrafast-rotating phase plates (URPPs) to achieve continuous spectral broadening of ultraviolet lasers. The rapid rotation elements behave as a random oscillator which induces Doppler frequency shift into the ultraviolet lasers. As an example, for a disk-shaped phase plate, with the beam acting on the edge at a radius of 10 cm, a rotation frequency of 1 kHz, and a phase-element size of 10 nm, the continuous spectral broadening reaches 0.07%. Further increasing the rotation speed or reducing the phase-element can lead to greater spectral broadening. When multiple URPPs are arranged in series, the superimposed spatiotemporal modulation further enhances the continuous spectral broadening and achieves more effective speckle smoothing. The scheme is applicable to broadening the independent spectrum of optical frequency combs as well as to the mitigation of laser-plasma instabilities in inertial fusion energy.

To address the non-optimal global design caused by the independent optimization of optical lenses, photodetectors, and computational processing subsystems in traditional remote sensing imaging system design, this paper proposes a holistic information theory for spatial remote sensing imaging. This theory integrates the optoelectronic imaging hardware front end and computational reconstruction back end into a unified framework. It establishes a complete spatial imaging chain information transfer model with the objective of obtaining the required effective information. The paper innovatively proposes a quantifiable Modulation Transfer Function (MTF)-Signal-to-Noise Ratio (SNR) product criterion. It demonstrates that the system information transmission ability is determined by the product of MTF and SNR, and that these parameters can compensate for each other to achieve equivalent information transfer. Validation through a high-resolution Earth observation system case shows that under consistent reconstruction mean square error conditions, increasing time delay integration stages reduces optical aperture size and significantly lowers primary mirror mass. Simulations and physical experiments further indicate that by increasing integration time, low-resolution optical systems achieve reconstructed fidelity comparable to high-resolution systems. This verifies that small-aperture optical systems can achieve equivalent imaging performance by enhancing SNR. This theory has been successfully applied in the design of the Jilin-1 satellite constellation, providing a new paradigm for low-cost high-resolution remote sensing systems.

A optical waveform can be synthesized by complex-frequency waves as well as by real-frequency harmonic waves. While single complex-frequency wave with exponentially rising waveform can be perfectly absorbed in lossless structures. Here, we propose that diverse optical waveforms can be captured without any absorption through the synthesis of complex frequencies in a lossless system. The scattering matrix zeros of the system correspond to a set of complex frequencies with exponentially rising waveforms, each of which can be virtually and perfectly absorbed. Thus, an input waveform, decomposed into these complex frequencies automatically, can be captured without any absorption. Then, in a well-designed coupled cavity system, various waveforms such as exponentially decaying, Gaussian, rectangular, triangular, and randomly generated piecewise-linear profiles, are captured with high efficiency. The proposed mechanism has potential applications in enhancing light-matter interactions and optical energy storage.

This paper introduces Bidirectional Technologies (BiTs), which is defined as technology that addresses the challenges within the aerospace and climate sectors simultaneously. BiTs presents a means to meet global development agendas, in particular, the United Nations 2030 Agenda for Sustainable Development and the Space2030 Agenda. These frameworks position aerospace innovations as tools to address climate change, explicitly highlighting the shared challenges between both fields. This overlap presents an underexplored opportunity to develop BiTs. To explore this potential, the study conducts an extensive literature review to examine the challenges within four categories in both Earth and space contexts: life support, energy systems, monitoring and exploratory systems, and novel technologies. Key traits that influence the successful development of BiTs are then extracted. Based on these traits, a set of actionable recommendations is proposed to serve as a starting point for policymakers and engineers to adapt technologies beyond single-domain applications. Ultimately, this study presents BiTs as a solution to the interconnected challenges on Earth and in space, offering strategies that advance innovation while contributing to global sustainability efforts.

Faithful uncertainty quantification (UQ) is paramount in high stakes climate prediction. Deep ensembles, or ensembles of probabilistic neural networks, are state of the art for UQ in machine learning (ML) and are growing increasingly popular for weather and climate prediction. However, detailed analyses of the mechanisms, strengths, and limitations of ensembles in these complex problem settings are lacking. We take a step towards filling this gap by deploying deep ensembles for predictability analysis of the El-Niño Southern Oscillation (ENSO) in the Community Earth System Model 2 Large Ensemble (CESM2-LE). Principally, we show that epistemic uncertainty, modeled by ensemble disagreement, robustly signals predictive error growth associated with shifts in the distributions of monthly sea-surface temperature (SST), ocean heat content (OHC), and zonal surface wind stress ($\tau_x$) anomalies under a climate change scenario. Conversely, we find that aleatoric uncertainty, which remains a popular measure of model confidence, becomes less reliable and behaves counterintuitively under climate-change-induced distributional shift. We highlight that, because ensemble performance improvement relative to the expected single model scales with epistemic uncertainty, ensemble improvement increases with distributional shift from climate change. This work demonstrates the utility of deep ensembles for modeling aleatoric and epistemic uncertainty in ML climate prediction, as well as the growing importance of robustly quantifying these two forms of uncertainty under anthropogenic warming.

Climate models indicate a significant slowdown of the Atlantic Meridional Overturning Circulation (AMOC) in the future, and some suggest it may collapse irreversibly to a substantially weakened state. The global warming threshold where this may happen is highly uncertain. An alternative to model-based threshold estimation are early-warning signals (EWS) in AMOC fingerprints, which can predict destabilization of a steady state (a saddle-node bifurcation) from generic changes in statistical properties. But an AMOC collapse may be a sequence of partial weakenings with shutdown of deep water formation in distinct regions. A conceptual model featuring sequential tipping points in two such regions is presented. Since the system only follows the expected saddle-node normal form when very close to each tipping point, a variety of trends in EWS are seen for different observables. This makes it hard to determine what type of collapse (partial or full) will follow, and when it will happen.

We present a new asymptotic strategy for general micro-macro models which analyze complex viscoelastic fluids governed by coupled multiscale dynamics. In such models, the elastic stress appearing in the macroscopic continuum equation is derived from the microscopic kinetic theory, which makes direct numerical simulations computationally expensive. To address this challenge, we introduce a formal asymptotic scheme that expands the density function around an equilibrium distribution, thereby reducing the high computational cost associated with the fully coupled microscopic processes while still maintaining the dynamic microscopic feedback in explicit expressions. The proposed asymptotic expansion is based on a detailed physical scaling law which characterizes the multiscale balance at the fast relaxation limit of the microscopic state. An asymptotic closure model for the macroscopic fluid equation is then derived according to the explicit asymptotic density expansion. Furthermore, the resulting closure model preserves the energy-dissipation law inherited from the original fully coupled multiscale system. Numerical experiments are performed to validate the asymptotic density formula and the corresponding flow velocity equations in several micro-macro models. This new asymptotic strategy offers a promising approach for efficient computations of a wide range of multiscale complex fluids.

We read Karen Crowther's Another 100 Years of Quantum Interpretation? with two practical goals. First, we spell out what she means by interpretation”: an attempt to provide understanding (not just predictions), which may be representationalist or non-representationalist, and which she contrasts with deeper reductive (inter-theoretic) explanation – especially in the quantum-gravity setting. Second, we list twelve points where the paper's physics-facing wording could be sharpened. In our view, several claims are directionally well-motivated but stated more strongly than the underlying physics supports, or they run together distinct notions (e.g.\ degrees of freedom,” singularity,” and different senses of locality”) that need careful separation. We end by suggesting that the philosophical question is genuinely worthwhile, but the physics should be phrased more cautiously so that heuristic motivation is not mistaken for strict implication.

This study quantitatively evaluates the impact of MRI scanner magnetic field strength on the performance and generalizability of deep learning-based segmentation algorithms. Three publicly available MRI datasets (breast tumor, pancreas, and cervical spine) were stratified by scanner field strength (1.5T vs. 3.0T). For each segmentation task, three nnU-Net-based models were developed: A model trained on 1.5T data only (m-1.5T), a model trained on 3.0T data only (m-3.0T), and a model trained on pooled 1.5T and 3.0T data (m-combined). Each model was evaluated on both 1.5T and 3.0T validation sets. Field-strength-dependent performance differences were investigated via Uniform Manifold Approximation and Projection (UMAP)-based clustering and radiomic analysis, including 23 first-order and texture features. For breast tumor segmentation, m-3.0T (DSC: 0.494 [1.5T] and 0.433 [3.0T]) significantly outperformed m-1.5T (DSC: 0.411 [1.5T] and 0.289 [3.0T]) and m-combined (DSC: 0.373 [1.5T] and 0.268[3.0T]) on both validation sets (p<0.0001). Pancreas segmentation showed similar trends: m-3.0T achieved the highest DSC (0.774 [1.5T], 0.840 [3.0T]), while m-1.5T underperformed significantly (p<0.0001). For cervical spine, models performed optimally on same-field validation sets with minimal cross-field performance degradation (DSC>0.92 for all comparisons). Radiomic analysis revealed moderate field-strength-dependent clustering in soft tissues (silhouette scores 0.23-0.29) but minimal separation in osseous structures (0.12). These results indicate that magnetic field strength in the training data substantially influences the performance of deep learning-based segmentation models, particularly for soft-tissue structures (e.g., small lesions). This warrants consideration of magnetic field strength as a confounding factor in studies evaluating AI performance on MRI.

Local interscale energy transfer in Large Eddy Simulation (LES) is typically diagnosed using the subgrid-scale (SGS) production, $\Pi^{SGS}$. In this work, an exact algebraic gauge identity is derived, demonstrating that $\Pi^{SGS}$ is composed of a kernel-integrated increment-based transfer, $\Pi^{inc}$, and the divergence of a spatial transport current, $\nabla \cdot J$. This identity was verified to machine precision ($10^{-16}$) using the analytical multi-harmonic Womersley solution. Further evaluation was conducted via Direct Numerical Simulation (DNS) of turbulent channel flow at $Re_\tau \approx 1000$. It was observed that $\nabla \cdot J$ dominates $\Pi^{SGS}$ in the near-wall region. The results suggest that $\Pi^{SGS}$ is not a unique proxy for the local cascade in inhomogeneous flows. A new framework is thus provided for the interpretation of interscale transfer diagnostics in wall-bounded transport phenomena.

Electrospray thrusters promise compact, high specific impulse propulsion for small spacecraft, yet ground characterization remains confounded by secondary species emission and incomplete diagnostics of neutral products. To address these limitations, we perform energy-resolved mixed quantum/classical (QM/MM) ab initio molecular dynamics (MD) of neutral 1-ethyl-3-methylimidazolium tetrafluoroborate, EMI-BF4, colliding with Au extractor surfaces with impact energies from 10 to 100 eV to resolve fragment species spectra, charge states, kinetic energy partitioning, and scattering geometry. The simulations reveal a three-stage sequence with impact energy: the low energy regime, 10 to 20 eV, which favors ionic dissociation, intermediate energy regime, between 30 to 40 eV, opens a neutralization window, and high energy regime, greater than 50 eV, drives covalent fragmentation into many light products with mixed charge states. Fractional energy distributions show a transition from few-body, energy-concentrated outcomes in the low energy regime to many-body, energy-dispersed outcomes in the high energy regime. Deflection angle distributions exhibit a strong mass-to-angle anti-correlation such that heavier fragments favor small deflection, whereas lighter fragments populate larger deflection angles. The fraction of transient metastables peaks near 50 eV, coinciding with abundant neutral fragment production. Importantly, neutral bombardment still produces charged secondaries at the target even when the upstream ion plume is fully suppressed by a decelerating electrode. These findings provide a basis for de-biasing facility measurements by pairing tandem time-of-flight secondary ion mass spectrometry and residual gas analyzer with suppression-bias corrections to inform the design of electrospray thrusters that reduce interception and contamination on extractor surfaces.

Cosmic-ray muons are natural probes for non-destructive imaging, but reaching sub-millimetre resolution with realistic exposure times has long been hindered by a fundamental limitation: the stochastic nature of multiple Coulomb scattering defies deterministic reconstruction of particle trajectories inside matter. Here we introduce Projection-shifted MUon transMission tomogrAphy (P$\mu$MA). Rather than localizing individual scattering points, P$\mu$MA records how material perturbations statistically shift the projected transmission tracks of a muon flux onto a virtual imaging plane. Near density boundaries, asymmetric projection-shift statistics naturally produce steep undershoot-overshoot profiles, sharpening edges without additional filtering. Crucially, the method operates robustly even with only two detector planes, a configuration where conventional scattering tomography cannot be applied. Cosmic-ray simulations with a lead knife-edge target yield edge widths as narrow as 1.196 mm, while monoenergetic-beam simulations reach 48 $\mu$m. Using a prototype system, we resolve 2-mm copper letters within two days—a feat unattainable by standard approaches under equivalent conditions. The projection-shift concept can be extended to accelerator- or laser-driven muon beams and to other ions, establishing a generalisable strategy for high-resolution particle-flow imaging.

Symmetry-protected ideal flat bands in one-dimensional (1D) Hermitian lattices are populated by compact localized states (CLS) - a special class of localization with wavefunctions confined within a small region. In this work, we discover that the non-Hermitian skin effect (NHSE) can appear in a flat band. Unlike conventional NHSEs for dispersive bands that are protected by nontrivial point-gap topology, the flat band remains a point on the complex-energy plane and is therefore always topologically trivial. We found that, intriguingly, the flat-band skin effect (FBSE) is associated with the non-trivial spectral topology of the dispersive bands enclosing the flat band on the complex-energy plane, so it only emerges within a finite range of non-Hermitian parameters and can counterintuitively disappear at large non-Hermiticity. Moreover, the gaps between the flat and the dispersive bands can close at higher-order exceptional points under both periodic and open boundary conditions. The flat-band wavefunctions are discontinuous in quantum distance across these exceptional points, signifying that the gap-closing is singular. The FBSE was experimentally observed in a non-Hermitian mechanical lattice. Our work reveals flat-band phenomena unique to non-Hermitian systems and highlights new possibilities in quantum geometry and localization control.

We present a simple experiment that can be run in class or remotely with minimal materials. Students use the principles of geometric optics to create their own versions of the bat signal.

In this thesis, we review the theory of Lyapunov exponents and covariant Lyapunov vectors (CLVs) and use these objects to numerically investigate the dynamics of several autonomous Hamiltonian systems. The algorithm which we use for computing CLVs is the one developed by Ginelli and collaborators (G&C), which is quite efficient and has been used previously in many numerical investigations. Using two low-dimensional Hamiltonian systems as toy models, we develop a method for measuring the convergence rates of vectors and subspaces computed via the G&C algorithm, and we use the time it takes for this convergence to occur to determine the appropriate transient time lengths needed when applying this algorithm to compute CLVs. The tangent dynamics of the centre subspace of the Hénon-Heiles system is investigated numerically through the use of CLVs, and we propose a method that improves the accuracy of the centre subspace computed with the G&C algorithm. As another application of the method of CLVs to the Hénon-Heiles system, we find that the splitting subspaces (which form a splitting of the tangent space and define the CLVs) become almost tangent during sticky regimes of motion, an observation which is related to the hyperbolicity of the system. Additionally, we investigate the dynamics of bubbles (i.e. thermal openings between base pairs) in homogeneous DNA sequences using the Peyrard-Bishop-Dauxois lattice model of DNA. For the purpose of studying short-lived bubbles in DNA, the notions of instantaneous Lyapunov vectors (ILVs) are introduced in the context of Hamiltonian dynamics. While we find that the size of the opening between base pairs has no clear relationship with the spatial distribution of the first CLV at that site, we do observe a distinct relationship with various ILV distributions.

We propose that spacetime dynamics can be organized by a Planck-scale bookkeeping rule, written using a modular-parameter normalization of size $2\pi$, that balances the geometric entropy increment $\delta A/4G$ against a reversible modular-energy flow $\delta\!\langle K\rangle$ and an irreversible Landauer–Bennett cost $\ln 2\,\delta N_c$, where $K\equiv K_\sigma$ is the (dimensionless) modular Hamiltonian of the chosen region defined relative to a fixed reference state $\sigma$, and $N_c$ counts logically irreversible one-bit record updates (e.g. coarse-grained overwrites or registrations) on that screen. This “information–geometry ledger” is consistent with the Bekenstein–Hawking area law and, when enforced on small causal screens under the standard entanglement-equilibrium assumptions, recovers the full nonlinear Einstein equation. In FLRW cosmology, the same bookkeeping motivates a two-component vacuum sector $\rho_{\rm vac}=\rho_\Lambda+3\varepsilon H^{2}/8\pi G$ when a constant inefficiency parameter $\varepsilon$ is assumed.

Light interacting with plant leaves undergoes reflection, transmission, scattering, and absorption, which together determine leaf optical properties. Changes in leaf architecture disrupt internal light scattering dynamics and consequently affect photosynthetic performance. Previous studies on internal leaf light scattering have primarily relied on ray-tracing approaches (e.g., Raytran) or radiative-transfer models (e.g., PROSPECT). However, these high-frequency approximations cannot capture diffraction and coherent multiple scattering in wavelength-scale leaf tissues, unlike full-wave electromagnetic simulations. Here, we employ GPU-accelerated Finite-Difference Time-Domain (FDTD) simulations to model internal light scattering dynamics using segmented cross-section image geometries of representative dicot and monocot leaves with wavelength-dependent complex refractive indices. The simulations accurately reproduce the reflectance and transmittance characteristics of healthy leaves, showing strong agreement with the PROSPECT model, with average Lin's concordance values of 0.8962 for dicot leaves and 0.7849 for monocot leaves. We further simulate early-stage necrotrophic fungal infection by modeling melanized hyphae penetrating the cuticle and upper epidermis. Diseased leaves exhibit a pronounced reduction in visible green reflectance and a marked suppression of the near-infrared reflectance plateau, consistent with experimental observations. Remaining discrepancies in the visible band are expected to be reduced through more advanced geometric and material modeling. This proof-of-concept study presents a full-wave FDTD optical modeling framework for plant-leaf light scattering, enabling physics-based analysis of internal scattering before and after early-stage fungal penetration and supporting the use of light scattering as an indicator for pre-symptomatic plant fungal disease detection.

Dualities in physics have challenged traditional forms of scientific realism by undermining the idea that theories describe a unique underlying ontology. In this paper, we develop a new perspective on scientific realism that responds to this challenge. We argue that while realist commitment remains appropriate at the level of a theory's full formal structure, ontological commitment should be treated as tied to specific empirical contexts rather than to a fixed, real ontology. Our proposal draws inspiration from Dennett's notion of a "compression algorithm" as a defining criterion of a scientific theory. On this basis, we separate realism from ontological commitment. To clarify the stakes of this distinction, we contrast our approach with common core realism, which locates ontology in the invariant structure shared by dual models. Focusing on dualities in quantum mechanics and string theory, we show how our view accommodates ontological pluralism while preserving a robust form of structural realism.

The operational lifetime of ionic liquid electrospray propulsion systems is limited by plume-extractor electrode interactions. Over time, propellant accumulation, surface erosion, and electrical shorts degrade the extractor and therefore restrict the total impulse throughput. Characterizing the secondary species generated by plume impacts with deposited ionic liquid is therefore essential to understanding and mitigating these degradation pathways. A surface analysis technique known as Time-of-Flight Secondary Ion Mass Spectrometry (TOF-SIMS), in the form of a custom electrospray laboratory diagnostic and an analytical-grade system, yields a comprehensive analysis of secondary ions formed from energetic ion beam impacts with ionic liquid thin-film substrates. Results revealed nearly identical positive secondary ion species for both EMI-BF4 and EMI-Im thin films, whereas EMI-Im produced a more diverse set of negative ions consistent with the greater chemical complexity of its anion. The analytical-grade SIMS spectra revealed many relatively high mass-to-charge ratio secondary ions likely below the detection limit for the laboratory diagnostic, thus shifting the average m/z value to above the monomer mass for most spectra. Finally, optical profilometry analysis reveals an estimated 0.5 nm/min sputter rate for an electrospray ion plume bombarding an ionic liquid thin film.

Over the past decade, multi-element superconducting nanowire single-photon detectors (SNSPDs) have emerged as the leading single-photon detection technology due to their exceptional system detection efficiency (SDE), ultrahigh timing precision, negligible dark counts, etc. However, achieving these performances with a single-element SNSPD has been an outstanding challenge due to a fundamental trade-off: a large active area is necessary for high SDE, while a smaller area is crucial for higher photon count rates, lower dark counts, and lower jitter. Here, we introduce an all-dielectric cryogenic metalens integrated with a single-element SNSPD to achieve high SDE with a smaller active area. Furthermore, we leverage a bifunctional metalens to demonstrate polarization-resolved photodetection at the telecommunication wavelength theoretically. Integrating multifunctional cryogenic metaurfaces with the state-of-the-art SNSPDs may enable novel capabilities with reduced size, weight, power, cost, and cooling (SWaP-C$^{2}$) requirements.

Due to climate change, heatwaves are becoming more frequent and intense, with western Europe experiencing the strongest trends in the Northern Hemisphere mid-latitudes. Part of the temperature trends are caused by circulation changes, which are not accurately captured in climate models. Here we deploy Deep Learning techniques to classify European heatwaves based on their atmospheric circulation and to study their associated changes over time. We use a Variational Autoencoder (VAE) to reduce the dimensionality of the heatwave samples, after which we cluster them on their extraced features. The VAE is trained on large ensemble climate model simulations and we show that the VAE generalizes well to observed heatwave circulations in ERA5 reanalysis, without the need for transfer learning. The circulation features relevant for heatwaves in ERA5 are consistent with the climate model heatwaves. Regression analysis reveals that the Atlantic Plume type of heatwaves are becoming more frequent over time, while the Atlantic High heatwaves are becoming less frequent. We introduce new and straightforward interpretability methods to study the latent space, including feature importance identification and changes over time. We investigate which circulation features are associated with the most important nodes in the latent space and how the latent space changes over time. For example, we find that the Atlantic Low heatwave shows a deepening of the low pressure system off the Atlantic coast over time. Each heatwave type is undergoing unique changes in their circulation, highlighting the necessity to study each heatwave type separately. Our method can furthermore be used to boost specific aspects of extreme events, and we illustrate how heatwave circulation could change in the future if the current trends persist, with in some cases an intensification of features.

Independent voters play an increasingly decisive role in contemporary elections, yet their collective behavior remains poorly understood. This paper investigates how a minority of voters with greater flexibility in their political preferences influences opinion formation in polarized electorates. Using a modified Deffuant model, we show that even simple heterogeneity in agents' openness to vote switching can generate rich and unexpected collective outcomes: "open-minded" agents may (i) prevent full convergence into established party blocs, (ii) give rise to transient centrist clusters, or (iii) align with the positions of major parties. These dynamics resemble empirical patterns observed among real-world independent voters. Our results demonstrate that small shifts in openness parameters can substantially reshape the macroscopic structure of political competition, offering a simple explanation for oscillatory electoral outcomes and the emergence of unstable centrist or cross-cutting coalitions.

A fundamental relation in Lagrangian Kolmogorov theory is concerned with inertial range scaling of the second-order velocity structure function over intermediate time lags at sufficiently high Reynolds numbers. Significant theoretical support for asymptotic constancy of the scaling constant ($C_0$) is known, but limitations in the range of time scales accessible in direct numerical simulation make unambiguous testing of the scaling challenging. In this paper, direct numerical simulations of forced isotropic turbulence at Taylor-scale Reynolds numbers between 140 and 1300 are used to improve understanding in this subject. Uncertainties arising from modest simulation time spans in the high Reynolds number data are addressed by expressing the velocity structure function in terms of the acceleration autocorrelation, which suggests that $C_0$ may be sensitive to effects of Lagrangian intermittency but does not rule out asymptotic constancy at Reynolds numbers beyond those that may be feasible in simulations in the foreseeable future. The Lagrangian velocity increment is examined further from a spatial-temporal perspective, as a combination of convective (spatial) and local (temporal) contributions, which are subject to a strong but incomplete mutual cancellation dependent on Reynolds number and time lag. The convective contribution is strongly influenced by the particle displacement, which is driven by large-scale dynamics and can thus grow into inertial range dimensions in space within just a few Kolmogorov time scales, without fully satisfying classical Lagrangian inertial-range requirements. An overall conclusion in this work is that both the limited range of time scales (narrower than that for length scales) and the effects of particle displacements have significant roles in the observed behavior of the second-order Lagrangian velocity structure function.

We employ correlation-consistent effective core potentials (ccECPs) to perform exact or nearly exact correlation and total energy calculations for the fifth-row elements (Rb-Xe). Total energies are calculated using various correlated methods: configuration interaction (CI), coupled-cluster (CC) up to perturbative quadruple excitations whenever feasible, and stochastic quantum Monte Carlo (QMC) approaches. In order to estimate the energy at the complete basis set (CBS) limit, the basis sets are constructed systematically through aug-cc-p(C)VnZ for each ccECP and further extrapolated to the CBS limit within the corresponding methods. Kinetic energies are evaluated at the FCI/CISD level to provide insights into the electron density and localization of the ccECPs. We also provide data sets for widely used diffusion Monte Carlo (DMC) to quantify fixed-node biases with single-reference trial wavefunctions. These comprehensive benchmarks validate the accuracy of ccECPs within the CC, CI, and QMC methodologies, thus providing accurate and tested valence-only Hamiltonians for many-body electronic structure calculations.

The Nobel Prize in Physics 2025 has been awarded to John Clarke, John Martinis, and Michel Devoret for "the discovery of macroscopic quantum mechanical tunnelling and energy quantisation in an electric circuit". The paper explains the essence of their studies and shows in a historical context the importance of earlier research in superconductivity and quantum physics of macroscopic systems by other physicists, particularly, Ukrainian scientists, including employees of the B. Verkin Institute for Low Temperature Physics and Engineering of the NAS of Ukraine (ILTPE). The role of the Fizyka Nyzkykh Temperatur (FNT) journal issued by ILTPE in Kharkiv and its translation as AIP Low Temperature Physics (LTP) in dissemination of results of the theoretical and experimental studies in the field is emphasized as well.

Bacteria commonly inhabit porous environments such as host tissues, soil, and marine sediments, where complex geometries constrain and redirect their motion. Although bacterial motility has been studied in porous media, the roles of cell length and pore shape in navigating these environments remain poorly understood. Here, we investigate how cell morphology and pore architecture jointly determine bacterial spreading behavior. Using genetically engineered E. coli with tunable cell length, we performed single-cell tracking in microfluidic devices that mimic ordered and disordered porous structures. We find that elongated bacteria traverse ordered pore networks more effectively than short cells, exhibiting straighter paths, greater directional persistence, and enhanced exploration efficiency. In contrast, in disordered porous media, elongated bacteria become trapped in dead-end regions for extended periods, resulting in markedly reduced navigational efficiency. Together, these results reveal how cell shape and environmental geometry interact to govern bacterial transport. Moreover, we suggest a new mechanism for separating antimicrobial-resistant (AMR) bacteria from elongated susceptible cells in designer porous media.

Squeezed light is a key resource in quantum metrology and quantum information science. It is primarily generated through nonlinear optical interactions, where the degree of squeezing is set by the nonlinearity of the medium. Here, we modulate the third-order nonlinear response of a dielectric with strong ultrafast laser fields to control squeezed light generation on attosecond time scales. By tuning a sub-cycle phase delay between the input femtosecond pulses, we switch the generated light between amplitude-squeezed and phase-squeezed states. We measure the quantum noise using a frequency-resolved balanced homodyne detection scheme that extracts field quadratures in many frequency modes simultaneously. From these measurements we obtain the complete coherency matrix containing quadrature correlations across the frequency modes of the pulse. These results enable quantum light sources with sub-cycle control of squeezing and open a route to transduction of dynamical quantum correlations in matter to quantum correlations in electric fields.