arXiv Physics @arxiv_physics@qoto.org

Removing carbon dioxide from the atmosphere may slow climate change and ocean acidification. My approach converts atmospheric carbon dioxide into graphite (CD2G). The net profit for this conversion is ~$381/ton CO2 removed from the atmosphere. At the gigaton scale, CD2G factories will increase the affordability and availability of graphite. Since graphite can be used to make thermal batteries and electrodes for fuel cells and batteries, CD2G factories will help lower the cost of storing renewable energy, which will accelerate the transition to renewable energy. Replacing fossil fuel energy with renewable energy will slow the release of carbon dioxide to the atmosphere, also slowing climate change. Converting atmospheric carbon dioxide into graphite will both generate a profit and slow climate change.

Microscale turbulent flow in porous media is conducive to the development of flow instabilities due to strong vortical and shearing flow occurring within the pore space. When the flow instabilities around individual solid obstacles interact with numerous others within the porous medium, unique symmetry-breaking phenomena emerge as a result. This paper focuses on investigations of the vortex dynamics and flow instabilities behind solid obstacles in porous media, emphasizing how solid obstacle geometry and porosity influence both microscale and macroscale flow behavior. Two distinct symmetry-breaking mechanisms were identified in different porosity ranges. In low porosity media (< 0.8), a "deviatory flow" phenomenon occurs, where the macroscale flow deviates from the direction of applied pressure gradient at Reynolds numbers above 500. Deviatory flow is a source of macroscale Reynolds stress anisotropy, which is counterbalanced by a diminished vortex core size. In the intermediate porosity regime (0.8-0.95), a "jetting flow" mechanism creates asymmetric microscale velocity channels in the pore space through temporally biased vortex shedding, occurring during the transition to turbulence. Both symmetry-breaking phenomena are critically influenced by solid obstacle shape, porosity, and Reynolds number. Circularity of solid obstacle geometry and an adequately high Reynolds number provide critical conditions for symmetry-breaking, whereas porosity can be used to parametrize the degree of symmetry-breaking. This paper provides fundamental insights into the intricate flow dynamics in porous media, offering a comprehensive understanding of how microscale vortex interactions generate macroscale flow asymmetries across different geometric configurations.

High-harmonic upconversion driven by a mid-infrared femtosecond laser can generate coherent soft X-ray beams in a tabletop-scale setup. Here, we report on a compact ytterbium-pumped optical parametric chirped pulse amplifier (OPCPA) laser system seeded by an all-fiber front-end and employing periodically-poled lithium niobate (PPLN) nonlinear media operated near the pulse fluence limits of current commercially available PPLN crystals. The OPCPA delivers 3 $μ$m wavelength pulses with 775 $μ$J energy at 1 kHz repetition rate, with transform-limited 120 fs pulse duration, diffraction-limited beam quality, and ultrahigh 0.33% rms energy stability over >18 hours. Using this laser, we generate soft X-ray high harmonics (HHG) in argon gas by focusing into a low-loss, high-pressure gas-filled anti-resonant hollow core fiber (ARHCF), generating coherent light at photon energies up to the argon L-edge (250 eV) and carbon K-edge (284 eV), with high beam quality and ~1% rms energy stability. This work demonstrates soft X-ray HHG in a high-efficiency guided-wave phase matched geometry, overcoming the high losses inherent to mid-IR propagation in unstructured waveguides, or the short interaction lengths of gas cells or jets. The ARHCF can operate long term without damage, and with the repetition rate, stability and robustness required for demanding applications in spectro-microscopy and imaging. Finally, we discuss routes for maximizing the soft X-ray HHG flux by driving He at higher laser intensities using either 1.5 $μ$m or 3 $μ$m - the signal and idler wavelengths of the laser.

Convolutional operations are computationally intensive in artificial intelligence services, and their overhead in electronic hardware limits machine learning scaling. Here, we introduce a photonic joint transform correlator (pJTC) using a near-energy-free on-chip Fourier transformation to accelerate convolution operations. The pJTC reduces computational complexity for both convolution and cross-correlation from O(N4) to O(N2), where N2 is the input data size. Demonstrating functional Fourier transforms and convolution, this pJTC achieves 98.0% accuracy on an exemplary MNIST inference task. Furthermore, a wavelength-multiplexed pJTC architecture shows potential for high throughput and energy efficiency, reaching 305 TOPS/W and 40.2 TOPS/mm2, based on currently available foundry processes. An efficient, compact, and low-latency convolution accelerator promises to advance next-generation AI capabilities across edge demands, high-performance computing, and cloud services.

Boron, nitrogen and carbon are neighbors in the periodic table and can form strikingly similar twin structures-hexagonal boron nitride (hBN) and graphene-yet nanofluidic experiments demonstrate drastically different water friction on them. We investigate this discrepancy by probing the interfacial water and atomic-scale properties of hBN using surface-specific vibrational spectroscopy, atomic-resolution atomic force microscopy (AFM), and machine learning-based molecular dynamics. Spectroscopy reveals that pristine hBN acquires significant negative charges upon contacting water at neutral pH, unlike hydrophobic graphene, leading to interfacial water alignment and stronger hydrogen bonding. AFM supports that this charging is not defect-induced. pH-dependent measurements suggest OH- chemisorption and physisorption, which simulations validate as two nearly equally stable states undergoing dynamic exchange. These findings challenge the notion of hBN as chemically inert and hydrophobic, revealing its spontaneous surface charging and Janus nature, and providing molecular insights into its higher water friction compared to carbon surfaces.

Traditional atomistic machine learning (ML) models serve as surrogates for quantum mechanical (QM) properties, predicting quantities such as dipole moments and polarizabilities, directly from compositions and geometries of atomic configurations. With the emergence of ML approaches to predict the "ingredients" of a QM calculation, such as the ground state charge density or the effective single-particle Hamiltonian, it has become possible to obtain multiple properties through analytical physics-based operations on these intermediate ML predictions. We present a framework to seamlessly integrate the prediction of an effective electronic Hamiltonian, for both molecular and condensed-phase systems, with PySCFAD, a differentiable QM workflow that facilitates its indirect training against functions of the Hamiltonian, such as electronic energy levels, dipole moments, polarizability, etc. We then use this framework to explore various possible choices within the design space of hybrid ML/QM models, examining the influence of incorporating multiple targets on model performance and learning a reduced-basis ML Hamiltonian that can reproduce targets computed from a much larger basis. Our benchmarks evaluate the accuracy and transferability of these hybrid models, compare them against predictions of atomic properties from their surrogate models, and provide indications to guide the design of the interface between the ML and QM components of the model.

The inverse design of metamaterial architectures presents a significant challenge, particularly for nonlinear mechanical properties involving large deformations, buckling, contact, and plasticity. Traditional methods, such as gradient-based optimization, and recent generative deep-learning approaches often rely on binary pixel-based representations, which introduce jagged edges that hinder finite element (FE) simulations and 3D printing. To overcome these challenges, we propose an inverse design framework that utilizes a signed distance function (SDF) representation combined with a conditional diffusion model. The SDF provides a smooth boundary representation, eliminating the need for post-processing and ensuring compatibility with FE simulations and manufacturing methods. A classifier-free guided diffusion model is trained to generate SDFs conditioned on target macroscopic stress-strain curves, enabling efficient one-shot design synthesis. To assess the mechanical response of the generated designs, we introduce a forward prediction model based on Neural Operator Transformers (NOT), which accurately predicts homogenized stress-strain curves and local solution fields for arbitrary geometries with irregular query meshes. This approach enables a closed-loop process for general metamaterial design, offering a pathway for the development of advanced functional materials.

Synchronized short-term activity cycles are a prominent feature in the nests of certain ant species, but their functional significance remains controversial. This study investigates whether synchronization enhances spatial accessibility within ant nests, a factor critical for efficient task performance and colony fitness. We use the mobile oscillator model, a computational framework that simulates ant movement and synchronous activity, to examine the effect of synchronization on the spatial distribution of inactive ants, which act as obstacles to the movement of active ants. Our analysis reveals no significant effect of synchronization on the size of inactive ant clusters or on direct measures of spatial accessibility, such as the number of steps and net displacement during ant activity periods. These findings suggest that within the mobile oscillator model, synchronization does not directly enhance spatial accessibility, highlighting the need for more complex models to elucidate the intricate relationship between synchronization and spatial dynamics in ant colonies.

This experimental study aims to investigate the effect of different base blowing configurations on the aerodynamics of a squareback Ahmed body. Four different slot configurations through which air is injected were studied. Each configuration had the same blowing area, equivalent to 10$\%$ of the base area of the body, and was designated according to its geometric shape: square (S), vertical (V), cross (C) and horizontal (H). The same range of injected flow rates, $C_{q}$, was tested for each slot geometry at a Reynolds number $Re$= 65000, with corresponding wind tunnel measurements. Our experiments revealed the effect of blowing on the near wake and consequently on the base pressure and drag of the Ahmed body. In particular, the geometry of the slots was shown to be a crucial factor in influencing aerodynamics, especially at blowing flow rates close to $C_{q,opt}$, which is the blowing flow rate that provides the minimum drag coefficient. The centered square slot (S) is the configuration that achieves a greater drag reduction. This behavior is attributed to an elongation in the recirculation region behind the Ahmed body, a reduction in the backflow inside the recirculation bubble, and a decrease in the wake asymmetry associated with the Reflexional Symmetry Breaking (RSB) mode. Conversely, the vertically oriented slot geometries, such as the cross (C) and the vertical (V) configurations, showed limited drag reduction capability while maintaining or even intensifying the wake asymmetry. The horizontal (H) slot represented an intermediate case, mitigating the wake asymmetry to a large extent, but proving to be less effective in reducing the drag than the square case. The hierarchy of the blowing configurations was dictated by the modifications induced in the near wake behind the Ahmed body, which influenced the asymmetry of the wake and the filling/emptying of the recirculation region.

Nearly 110 million people are forcibly displaced people worldwide. However, estimating the scale and patterns of internally displaced persons in real time, and developing appropriate policy responses, remain hindered by traditional data streams. They are infrequently updated, costly and slow. Mobile phone location data can overcome these limitations, but only represent a population segment. Drawing on an anonymised large-scale, high-frequency dataset of locations from 25 million mobile devices, we propose an approach to leverage mobile phone data and produce population-level estimates of internal displacement. We use this approach to quantify the extent, pace and geographic patterns of internal displacement in Ukraine during the early stages of the Russian invasion in 2022. Our results produce reliable population-level estimates, enabling real-time monitoring of internal displacement at detailed spatio-temporal resolutions. Accurate estimations are crucial to support timely and effective humanitarian and disaster management responses, prioritising resources where they are most needed.

Migraine literacy among the public is known to be low, and this lack of understanding has a negative impact on migraineurs' quality of life. To understand this impact, we use text mining methods to study migraine discussion on the Reddit social media platform. We summarize the findings in the form of "four things people should know about chronic migraines": it is a serious disease that affects people of all ages, it can be triggered by many different factors, it affects women more than men, and it can get worse in combination with the COVID-19 virus.

We present the Quantum Memory Matrix (QMM) hypothesis, which addresses the longstanding Black Hole Information Paradox rooted in the apparent conflict between Quantum Mechanics (QM) and General Relativity (GR). This paradox raises the question of how information is preserved during black hole formation and evaporation, given that Hawking radiation appears to result in information loss, challenging unitarity in quantum mechanics. The QMM hypothesis proposes that space-time itself acts as a dynamic quantum information reservoir, with quantum imprints encoding information about quantum states and interactions directly into the fabric of space-time at the Planck scale. By defining a quantized model of space-time and mechanisms for information encoding and retrieval, QMM aims to conserve information in a manner consistent with unitarity during black hole processes. We develop a mathematical framework that includes space-time quantization, definitions of quantum imprints, and interactions that modify quantum state evolution within this structure. Explicit expressions for the interaction Hamiltonians are provided, demonstrating unitarity preservation in the combined system of quantum fields and the QMM. This hypothesis is compared with existing theories, including the holographic principle, black hole complementarity, and loop quantum gravity, noting its distinctions and examining its limitations. Finally, we discuss observable implications of QMM, suggesting pathways for experimental evaluation, such as potential deviations from thermality in Hawking radiation and their effects on gravitational wave signals. The QMM hypothesis aims to provide a pathway towards resolving the Black Hole Information Paradox while contributing to broader discussions in quantum gravity and cosmology.

The study examines how Gacha games affect gambling practices by analyzing when players start playing and how often they play and how much money they spend on games. The modified Problem Gambling Severity Index (PGSI) serves as the research tool to examine the impact of these variables on mobile Gacha game gambling-like severity. A survey distributed to online Gacha game communities provided the data which researchers analyzed by applying linear regression and multiple regression and T-test methods. The research indicates entry age demonstrated low association with gambling severity but gameplay frequency together with duration directly influenced higher gambling severity ratings. The research findings demonstrated that the level of Gacha game spending directly influences gambling severity scores among players. The research findings demonstrate how addictive potential exists in mobile Gacha games which requires stronger regulation of these platforms for young vulnerable gamers. Research needs to study the extended influence of Gacha game exposure while examining how cognitive biases affect gambling behavior patterns.

Biofilms exposed to flow experience shear stress, which leads to a competitive interaction between the growth and development of a biofilm and shearing. In this study, Pseudonomas fluorescene biofilm was grown in a microfluidic channel and exposed to forced flow of an aqueous solution of variable velocity. It can be observed that under certain conditions preferential flow paths form with a dynamic, but quasi-steady state interaction of growth, detachment, and re-attachment. We find that the regimes for preferential flow path development are determined by nutrient availability and the ratio of shear stress versus the biofilm's ability to resist shear forces. The intermittent regime of flow paths is mainly driven by the supply with nutrients, which we confirm by comparison with a numerical model based on coarse-grained molecular dynamics and Lattice Boltzmann hydrodynamics.

It is noteworthy that limiting compactness of a static bounded configuration is characterized by a general principle: \textit{one, by equipartition of mass between inside and outside, and the other by vanishing of energy inside.} The former implies gravitational energy being half of mass leading to limiting compactness $M/R = 4/9$ of Buchdahl star while for the latter, the two are equal giving $M/R = 1/2$ of black hole with horizon. \emph{This is the relativistic Virial theorem respectively for massive and massless particles.} It is remarkable that it prescribes that there can exist only two equilibrium states which also define limiting compactness of the object. Consequently, it leads to a profound prediction that the ultimate endproduct of gravitational collapse could only be one of the two, Buchdahl star or black hole.

We present the results of a novel classification scheme for all items, objects, concepts, and crucially -- things -- in the known and unknown universe. Our definitions of meat, soup and vegetable are near-exhaustive and represent a new era of scientific discovery within the rapidly-developing field of Arbitrary Classification. While the definitions of vegetable (growing in the ground), meat (growing in an animal) and soup (containing both vegetable and meat) may appear simple at first, we discuss a range of complex cases in which progress is rapidly being made, and provide definitions and clarifications for as many objects as a weekend of typing will allow.

Issuing timely severe weather warnings helps mitigate potentially disastrous consequences. Recent advancements in Neural Weather Models (NWMs) offer a computationally inexpensive and fast approach for forecasting atmospheric environments on a 0.25° global grid. For thunderstorms, these environments can be empirically post-processed to predict wind gust distributions at specific locations. With the Pangu-Weather NWM, we apply a hierarchy of statistical and deep learning post-processing methods to forecast hourly wind gusts up to three days ahead. To ensure statistical robustness, we constrain our probabilistic forecasts using generalised extreme-value distributions across five regions in Switzerland. Using a convolutional neural network to post-process the predicted atmospheric environment's spatial patterns yields the best results, outperforming direct forecasting approaches across lead times and wind gust speeds. Our results confirm the added value of NWMs for extreme wind forecasting, especially for designing more responsive early-warning systems.

Conductive ferroelectric domain walls (DWs) represent a promising topical system for the development of nanoelectronic components and device sensors to be operational at elevated temperatures. DWs show very different properties as compared to their hosting bulk crystal, in particular with respect to the high local electrical conductivity. The objective of this work is to demonstrate DW conductivity up to temperatures as high as 400 °C which extends previous studies significantly. Experimental investigation of the DW conductivity of charged, inclined DWs is performed using 5 mol% MgO-doped lithium niobate single crystals. Currentvoltage (IV) sweeps as recorded over repeated heating cycles reveal two distinct thermal activation energies for a given DW, with the higher of the activation energies only measured at higher temperatures. Depending on the specific sample, the higher activation energy is found above 160 °C to 230 °C. This suggests, in turn, that more than one type of defect/polaron is involved, and that the dominant transport mechanism changes with increasing temperature. First principles atomistic modelling suggests that the conductivity of inclined domain walls cannot be solely explained by the formation of a 2D carrier gas and must be supported by hopping processes. This holds true even at temperatures as high as 400 °C. Our investigations underline the potential to extend DW current based nanoelectronic and sensor applications even into the so-far unexplored temperature range up to 400 °C.

The concept of acoustoelasticity pertains to changes in elastic wave velocity within a medium when subjected to initial stresses. However, existing acoustoelastic expressions are predominantly developed for waves propagating parallel or perpendicular to the principal stress directions, where no shear stresses are involved. In our previous publication, we demonstrated that the impact of shear stresses on longitudinal wave velocity in concrete, when body waves propagate in the shear deformation plane, is negligible. This finding allows us to revise the acoustoelastic expression for longitudinal waves propagating inclined to the principal stress directions in stressed concrete. The revised expression reveals that the acoustoelastic effect for such longitudinal waves can be expressed using acoustoelastic parameters derived from waves propagating parallel and perpendicular to the uniaxial principal stress direction. To validate our theoretical statement, experiments were conducted on a concrete cylinder subjected to uniaxial stress. Despite slight fluctuations in the experimental observations, the overall trend of acoustoelastic effects for inclined propagating longitudinal waves aligns with the theory. This proposed theory holds potential for monitoring changes in the magnitudes and directions of principal stresses in the plane stress state.

In a recent work, arxiv:2412.20407, we have mentioned two Egyptian kings, Shepseskaf and Userkaf, of the IV and V Dynasties. Here we continue discussing their burial places, that is their pyramids, in a new framework based on the study of orientation of pyramid substructures (entrances, corridors, burial chambers). Shepseskaf and Userkaf opted for the same architectonic solutions, exhibiting a continuity in funerary architecture. After showing the substructure orientations of the pyramids of the IV and V Dynasties, we will stress that the substructure in the Shepseskaf's pyramid, the Mastabat Fara'un, is oriented as in the Fourth Dynasty architecture of pyramids and that it has a layout which is closer to that used by the following Fifth Dynasty. In our discussion, we will start with the first ruler of the Fourth Dynasty, Snefru, who introduced a new form of external and internal layout of the burial complexes of the king, through an evolution based on four attempts, the Seila, Meidum, Bent and Red pyramids. In the final model, the Snefru's Red pyramid, the substructure has a north entrance and a burial chamber east-west oriented. This orientation persisted in the pyramids of the Fourth and Fifth Dynasties, and beyond. The Sekhemkhet (Djoserty) pyramid of the Third Dynasty is also illustrated for comparison, such as some earlier burial monuments. In the discussion here proposed the position of sarcophagus and of the body inside it is also investigated. The sarcophagus has its axis north-south. The body of the deceased king was lying on its left side, extended, head to the north, face towards the east. In this framework, we propose again what we told in arXiv 2016, arxiv:1604.05963, and the layout of pyramids with respect to sunrise on solstices.