This study presents a comprehensive first-principles investigation of the optoelectronic and thermoelectric properties of aluminum antimonide (AlSb) in its cubic (F-43m) and hexagonal (P63mc) phases. Structural optimization was performed using the SCAN functional, and all electronic and optical properties were evaluated using the modified Becke-Johnson potential combined with the Hubbard correction (mBJ+U), which best describes the band-edge electronic structure, explicitly accounting for the contribution of the d-states of the Sb half-core, which cannot be adequately accounted for by conventional functionals and may be overestimated by hybrid approaches. Both AlSb phases are found to be quasi-direct bandgap semiconductors, with calculated band gaps of 1.71 eV for the cubic phase and 1.50 eV for the hexagonal phase, in good agreement with available experimental data. The optical response reveals strong absorption in the visible and ultraviolet regions, moderate reflectivity, and high refractive indices, indicating pronounced light-matter interaction characteristic of III-V semiconductors. The hexagonal phase exhibits enhanced low-energy optical absorption due to its reduced symmetry and narrower band gap. Thermoelectric analysis demonstrates large negative Seebeck coefficients, thermally activated carrier generation, and a monotonic increase of the power factor with carrier concentration for both phases. The cubic phase shows higher power factor values due to enhanced carrier mobility, whereas the hexagonal phase benefits from reduced thermal conductivity, which is favorable for thermoelectric performance at elevated temperatures. These results establish AlSb as a multifunctional semiconductor with tunable optoelectronic and thermoelectric properties and highlight the importance of an accurate treatment of Sb d-electron effects for reliable property prediction.

Quantum information science and engineering (QISE) is advancing rapidly, creating an urgent demand for a quantum-literate, technically proficient workforce. Despite this need, quantum education initiatives remain fragmented across regions, educational levels, and instructional approaches, which constrains their scalability and overall impact. This paper offers a structured analysis of the current quantum education ecosystem by synthesizing global initiatives, pedagogical strategies, and emerging trends. Quantum education is examined through a dual framework that considers both learner progression and instructional methodology, emphasizing the evolution of educational approaches from conceptual exposure to formal reasoning and practical application. Analysis of data from international programs and academic literature reveals key challenges, including inequitable access, absence of standardized curricula, limited empirical evaluation, and discontinuities between educational stages. Quantum education is more accurately conceptualized as a non-linear ecosystem rather than a traditional pipeline, characterized by multiple entry points, feedback mechanisms, and critical transition gaps. Based on this perspective, directions are proposed for developing more coherent, inclusive, and scalable educational frameworks that align with workforce requirements and technological progress. This work presents a unified perspective on the quantum education landscape and outlines actionable strategies to enhance global quantum literacy and workforce preparedness.

For a quantum scalar field that is confined in a uniformly linearly accelerated cavity in Minkowski spacetime and interacts linearly with a scalar field that is not confined in the cavity, a de-excitation probability formula was obtained in [1] [K. Lorek et al, Class. Quant. Grav. 32, 175003 (2015) [arXiv:1503.01025]] by a first-order perturbation theory calculation. A recent Comment [2] [V. Toussaint, Class. Quant. Grav. 43, 068001 (2026)] questions this formula on the grounds that the calculation in [1] invokes Rindler modes both in the Rindler wedge of the accelerated cavity and in the opposing, causally disconnected Rindler wedge. In the present Reply we rederive the de-excitation formula given in [1] by a perturbation theory calculation that is formulated entirely within the Rindler wedge of the accelerated cavity. We also take the opportunity to comment on the role of the two sets of Rindler modes in the calculation presented in [1].

Life Cycle Assessment (LCA) is increasingly used to quantify and regulate the environmental impacts of Information and Communication Technology (ICT) systems. Since direct biosphere measurements are complicated to perform, we claim that the environmental impact assessment of ICT relies heavily on models. In this paper, we first revisit the fundamentals of LCA: we emphasize that ICT LCAs effectively form systems of models, and we argue that such systems require an extra-high level of carefulness in construction, calibration, integration, and interpretation. We then document how this level of rigor is challenging to achieve with current practices. This is illustrated with emblematic examples of model misuse and an analysis of structural challenges related to database choice, scope mismatches, opaque aggregation, and model integration. From this analysis, we derive four key requirements for credible ICT LCA: explicit model lineage, clearly defined model scope, end-to-end traceability, and managed non-obsolescence. Finally, we propose a framework that operationalizes these requirements using explicit dependency graphs, an open and versioned LCA-oriented model repository, automatic enforcement of integrity constraints, and a well-defined model taxonomy.

The $E_8 \times ωE_8$ octonionic program aims at a deliberately ambitious synthesis: quantum theory without external classical time, objective collapse, emergent classical space-time, exceptional Jordan-algebra flavor structure, and an exceptional-group unification of visible matter with a right-handed pre-gravitational sector. The purpose of the present paper is not to review the whole formalism, but to assemble in one place the claims that are experimentally vulnerable and to classify them by logical strength. We begin with a cold-start pedagogical map from the core ingredients of the program to the observables it claims to generate. This map matters because the program's breadth is both its attraction and its principal vulnerability: if the particle-physics, gravitation, and quantum-foundational claims are not visibly derived from the same structure, the framework reduces to a collection of disconnected conjectures. On the quantum-foundational side the program predicts objective spontaneous collapse, operator time, spontaneous collapse in time, loss of temporal interference above an attosecond-scale separation, a six-dimensional explanation of apparent nonlocality, possible Bell correlations beyond the Tsirelson bound, a fermion-only collapse sector, and holographic or Karolyhazy-type space-time uncertainty. In particle physics it predicts a right-handed pre-gravitational gauge sector, an extended Higgs sector, dark electromagnetism and its dark photon, three inert right-handed Majorana neutrinos, Majorana light neutrinos, a maximal leptonic Dirac phase, CKM root-sum rules, charged-fermion mass relations including the first-generation $1{:}4{:}9$ pattern and the relation $m_τ/m_μ= m_s/m_d$, a low-energy fine-structure constant, a weak-mixing-angle derivation, and the mixed-regime relation $α_s(M_Z)/α_{em}(0)=16$.

The frequency band between $0.1$ and $10\mathrm{Hz}$ remains largely unexplored in gravitational-wave astronomy due to strong seismic, Newtonian, and suspension thermal noise that limit ground-based detectors. The Cryogenic sub-Hz cROss torsion-bar detector with quantum NOn-demolition Speed meter (CHRONOS) is a novel detector concept designed to access this frequency range from the ground. CHRONOS combines cryogenic torsion-bar test masses with a triangular Sagnac interferometer implementing a speed-meter readout, which suppresses quantum radiation-pressure noise and enables quantum non-demolition measurements in the sub-Hz regime. The detector targets a strain sensitivity of $h \sim 10^{-18}\mathrm{Hz^{-1/2}}$ around $2\mathrm{Hz}$ and stochastic gravitational wave background of $Ω_{GW} \sim 2\times 10^{-3}$ at $2\mathrm{Hz}$. This sensitivity opens a new observational window between space-based detectors such as LISA and ground-based interferometers, enabling observations of intermediate-mass black hole binaries, searches for stochastic gravitational-wave backgrounds, and tests of macroscopic quantum measurements.

This note slightly strengthens the result of arXiv:2201.12295 on the linear instability of the Kerr Cauchy horizon. This strengthened result is used in the proof arXiv:2604.04877 of the non-linear instability of the Kerr Cauchy horizon.

We derive generalization error bounds for the training of two-layer neural networks without assuming boundedness of the loss function, using Wasserstein distance estimates on the discrepancy between a probability distribution and its associated empirical measure, together with moment bounds for the associated stochastic gradient method. In the case of independent test data, we obtain a dimension-free rate of order $O(n^{-1/2} )$ on the $n$-sample generalization error, whereas without independence assumption, we derive a bound of order $O(n^{-1 / ( d_{\rm in}+d_{\rm out} )} )$, where $d_{\rm in}$, $d_{\rm out}$ denote input and output dimensions. Our bounds and their coefficients can be explicitly computed prior to the training of the model, and are confirmed by numerical simulations.

The mass spectrum of pseudoscalar and scalar meson modes at finite temperature is studied in the framework of a nonlocal quark model. The model implements quark confinement via the modification of the Laplace transform of the quark propagator. In order to synchronize the confining and deconfining phases, a modification of the transform is proposed. The behavior of the screening masses of mesons is studied in a wide region of temperatures, while the pole masses are described up to the deconfining phase transition.

The Zero-trust (ZT) model is an increasingly popular model that relies on the idea that no trust should be granted to any entity (network, persons, devices) by default. ZT model is gaining attention from both research and practice, with various levels of adequation between research developed and real-life applications. NIST provided a standard to fulfill requirements of ZT architecture of network core but many practical aspects remain unspecified, some of them requiring solving first research challenges in order to be implemented efficiently. An example of such an unspecified field is the integration of IoT/Smart Peripheral Devices (SPD). Various reasons explain this gap: specificities of such resources (possibly lower energy/computation power), their lifecycle, and their use, strongly depending on the use of the whole platform IoT devices are part of. Moreover, additional difficulty to have a good understanding is induced by the fact that both Zero Trust and IoT are identified as promising trends in cybersecurity: many vendors/researchers tag their solutions as IoT integration into the ZT model, with little to no effective compliance to ZT model or standard. Industry is providing many practice-oriented literature, that has to be compared to academic work and standards, in order to consolidate the current state of knowledge and solutions offered to realize this integration. In this paper, we conduct a literature review of non-academic publications, in order to consolidate current knowledge, trends, and future challenges for the industrial integration of IoT devices in ZT architecture.

Efficient data encoding is the main factor affecting how fast hybrid quantum-classical algorithms run, but traditional simulators spend most of their time changing classical features into quantum rotations. This work introduces Hybriqu Encoder, a Rust-based, SIMD-aware kernel that focuses exclusively on angle encoding and integrates transparently with Python via CFFI. The kernel processes four double-precision rotations at once using AVX-class vector lanes, combines data in a way that fits well with the cache and uses pre-calculated trigonometric factors, while keeping all unsafe operations within a safe Rust interface. Benchmarks on Apple Silicon show that using pure angle encoding is 5.4% faster at 64 qubits, and the speedup increases as the amount of data exceeds the L1 cache size, while kernels that quickly apply rotations to the entire state vector are limited by memory and do not benefit from SIMD. These results indicate that using vectorization leads to consistent improvements when calculations are the main focus, but limits on data transfer speed prevent additional speed increases, highlighting the need for future efforts on better state updates and choosing between different processing methods. By combining smart optimization that considers the architecture with Rust's safety features, the Hybriqu Encoder offers a flexible base for bigger, mixed systems designed to reduce data encoding delays in future hybrid quantum-classical processes.

We examine several observable optical properties of a Skyrmion black hole (BH), focusing on the photon sphere, BH shadow, and photon trajectories. The Skyrme term, along with other geometric parameters of the spacetime, determines the photon sphere location and shapes the resulting BH shadow. Parameter variations produce observable departures from standard BH geometries, offering potential signatures of nonlinear field effects. We also analyze the sparsity of Hawking radiation and the associated energy emission spectra, showing how these quantities respond to the Skyrme coupling and background parameters. Our findings illuminate the connection between nonlinear field contributions and BH optics, with implications for observational and theoretical studies of modified gravity scenarios.

Floating-point arithmetic is error-prone and unintuitive. Floating-point debuggers instrument programs to monitor floating-point arithmetic at run time and flag numerical issues. They estimate residues, i.e., the difference between actual floating-point and ideal real values, for every floating-point value in the program. Prior work explores various approaches for computing these residues accurately and efficiently. Unfortunately, the most efficient methods, based on "error-free transformations", have a high rate of false reports, while the most accurate methods, based on high-precision arithmetic, are very slow. This paper builds on error-free-transformations-based approaches and aims to improve their accuracy while preserving efficiency. To more accurately compute residues, this paper divides residue computation into two steps (rounding error computation and residue function evaluation) and shows how to perform each step accurately via careful improvements to the current state of the art. We evaluate on 44 large scientific computing workloads, focusing on the 14 benchmarks where prior tools produce false reports: our approach eliminates false reports on 10 benchmarks and substantially reduces them on the remaining 3 benchmarks. Moreover, complex numerical issues require additional care due to absorption, where two machine-precision residues cannot both be computed accurately in a single execution. This paper introduces residue override, which re-executes the program multiple times, computing different residues in different executions and assembling a final "patchwork" execution. We evaluate on 169 standard benchmarks drawn from numerical analysis papers and textbooks, requiring only 3.6 re-executions on average. Among 34 benchmarks with false reports in the initial run, residue override is triggered on 29 of them and reduces false reports on 25 of them, averaging 7.1 re-executions.

Purpose: Volumetric ultrafast ultrasound produces massive datasets with high frame rates, dense reconstruction grids, and large channel counts. Beamforming computational demands limit research throughput and prevent real-time applications in emerging modalities such as elastography, functional neuroimaging, and microscopy. Approach: We developed mach, an open-source, GPU-accelerated beamformer with a highly optimized delay-and-sum CUDA kernel and an accessible Python interface. mach uses a hybrid delay computation strategy that substantially reduces memory overhead compared to fully precomputed approaches. The CUDA implementation optimizes memory layout for coalesced access and reuses delay computations across frames via shared memory. We benchmarked mach on the PyMUST rotating disk dataset and validated numerical accuracy against existing open-source beamformers. Results: mach processes 1.1 trillion points per second on a consumer-grade GPU, achieving $>$10$\times$ faster performance than existing open-source GPU beamformers. On the PyMUST rotating disk benchmark, mach completes reconstruction in 0.23~ms, 6$\times$ faster than the acoustic round-trip time to the imaging depth. Validation against other beamformers confirms numerical accuracy with errors below $-60$~dB for Power Doppler and $-120$~dB for B-mode. Conclusions: mach achieves 1.1 trillion points per second throughput, enabling real-time 3D ultrafast ultrasound reconstruction for the first time on consumer-grade hardware. By eliminating the beamforming bottleneck, mach enables real-time applications such as 3D functional neuroimaging, intraoperative guidance, and ultrasound localization microscopy. mach is freely available at https://github.com/Forest-Neurotech/mach

The exponential growth of Common Vulnerabilities and Exposures (CVE) disclosures poses significant challenges for enterprise security management, necessitating automated and quantitative risk assessment methodologies. Existing vulnerability analysis approaches suffer from three critical limitations: (1) lack of systematic severity quantification models that integrate heterogeneous attack attributes, (2) insufficient exploration of latent correlations among risk factors, and (3) absence of cumulative risk distribution analysis for prioritized remediation. To address these challenges, we propose MVRAF (Multi-dimensional Vulnerability Risk Assessment Framework), a comprehensive data-driven framework for large-scale CVE security analysis. Our framework introduces three key innovations: (1) a Vulnerability Severity Quantification Model that transforms CVSS attributes into normalized risk metrics through weighted aggregation of exploitability and CIA impact scores, (2) a Risk Factor Correlation Analysis module that captures statistical dependencies among attack vectors, complexity, and privilege requirements via correlation matrices, and (3) an Empirical Risk Distribution mechanism that enables cumulative threat assessment for resource allocation optimization. Extensive experiments on 1,314 real-world CVE records from the National Vulnerability Database demonstrate that our framework effectively identifies risk hotspots, with 46.2% of network-based vulnerabilities classified as high-risk and strong correlations observed between CIA impacts and overall severity scores.

Second-order ordinary linear differential equations appear ubiquitously across physics, describing the behavior of systems from the quantum world of atoms to the classical world of gravitating bodies. We present a unified symmetry-based framework that provides a ``litmus-test criterion'' to determine when such a system admits a hierarchical ladder structure, and, whenever it does, explicitly constructs the ladder. This approach uncovers a previously underappreciated connection to supersymmetric quantum mechanics and a deep commonality among diverse physical problems. Applications to the quantum harmonic oscillator and dynamical tidal response of Kerr black holes are presented to illustrate the framework.

By combining methods from thermal field theory and statistical mechanics, we reexamine the spin polarization caused by the relativistic Barnett effect in a rigidly rotating Fermi gas. We determine the pressure of this medium and show that it depends on an effective chemical potential, which includes contributions from orbital angular momentum-rotation and spin-rotation coupling. We introduce a specific regularization scheme to sum over the angular momentum quantum numbers. As a result, the thermal pressure and all thermodynamic quantities are separated into two parts that differ only in the spin fugacities of spin-up and spin-down fermions. We calculate the Fermi energy for both components and show that the Fermi energy of the spin-down fermions is lower than that of the spin-up ones. This difference arises from the spin-rotation coupling and leads to a spin polarization consistent with the Barnett effect. In particular, we introduce the spin-chemicorotational ratio $η\equiv Ω^{(0)}/2μ^{(0)}$, which adjusts the spin polarization of the Fermi gas. Here, $Ω^{(0)}$ and $μ^{(0)}$ represent the angular velocity and chemical potential at zero temperature, respectively. The factor $1/2$ accounts for the fermion's spin. We explore the temperature dependence of $μ$ and $Ω$, while assuming that the number of spin-up and spin-down fermions remains temperature independent. Our findings indicate that the spin-down component of the rotating Fermi gas dilutes at lower temperatures compared to the spin-up component. Additionally, we calculate the magnetic susceptibility arising from the Barnett magnetization and demonstrate that it is proportional to the moment of inertia $I$ of the rotating Fermi gas. Finally, we prove that $I$ exhibits a $1/T$ behavior in the high-temperature limit, similar to the Curie law of paramagnetism.

We present the material and resources developed for training physicists on containerization technologies enabled by Apptainer. In the context of analysis preservation using Apptainer's capabilities, we have developed examples that execute common tools in High Energy Physics (HEP) and Nuclear Physics within containers. Training physicists on containerization technologies is of utmost importance in today's research landscape. By embracing these technologies, users can achieve enhanced reproducibility, portability, collaboration, and resource efficiency, assuring the conditions and integrity of the scientific analysis process. This training module,``Introduction to Apptainer/Singularity'', is part of the HEP Software Foundation Training Center, which aims to equip newcomers to the field of High Energy Physics with the necessary software skills and best practices.

Andrews, Dixit, Schultz, and Yee conjecture the parity of a double Lambert series. In 2026, Amdeberhan, Andrews, and Ballantine offer some ideas that are pointing in the right direction for the proof. In this paper, we complete the rest of their proof.

AI IDEs and coding agents compress discovery, fetch, workspace open, installation, and execution into one low-observability loop. Existing defenses such as provenance frameworks, package and repository firewalls, runtime protection, and tool-approval prompts each cover part of that path, but they often leave the final consumer-side execution decision implicit. ZitPit is a 100% open-source Rust system that argues for a stricter boundary: first-seen external artifacts should become durable policy events before they gain execution rights on protected developer or CI hosts. The current public evidence is intentionally narrow and explicit. It includes repeated Git smart-HTTP intake measurements showing that approved artifacts can remain faster than unmanaged public fetch, plus implemented protected-session and governed-egress proof families. The broader contribution is architectural rather than universal-coverage-by-assertion: ZitPit unifies artifact admission, repo-open state, capability-scoped execution, and durable policy records at the consumer execution boundary for agentic workflows.

We prove that the ratio $B_n/D_n$ of the Apéry-like sequence $B_n$ to the Domb numbers $D_n$ converges to $(7/24)ζ(3)$, and that $\sum_{n=1}^{\infty} 64^n/(n^3 D_n D_{n-1}) = (56/3)ζ(3)$. As a corollary we establish the value $Z_2 = 12/(7ζ(3))$ conjectured by the Ramanujan Machine project. The proof uses level-6 eta products, Atkin--Lehner involutions, and Eichler integrals of weight-4 modular forms.

We prove that the symmetric-cube coefficients $A_n = (-27)^n [z^n] {}_2F_1(1/3,1/3;1;z)^3$ satisfy the supercongruence $A_{mp} \equiv A_m \pmod{p^4}$ for every prime $p \geq 5$ and every positive integer $m$. The proof proceeds by establishing an order drop from 3 to 2 via Ore factorization, deriving the full modular dictionary on $X_0(3)$ with logarithmic derivative $C(q) = 3E_{5,χ_0,χ_3}(q)$, and combining a Lagrange--Bürmann extraction with a three-layer exponential truncation. The defect forms are killed by a Fricke--Hecke intertwining argument using the cusp filtration at the second cusp of $X_0(3)$.

The Hofstadter Q-sequence is a prominent example of nested recurrence. Despite decades of study, it is not even known whether Q(n) is defined for all n. Mantovanelli introduced a parity-perturbed variant $\widetilde{Q}$, obtained by adding $(-1)^n$ to the recursion, which surprisingly replaces the chaotic behaviour of Q by an exact dyadic self-similarity. In this paper we prove that $\widetilde{Q}$ is well-defined for all n and satisfies $|\widetilde{Q}(n)/n - 1/2| = O(1/\sqrt{\log n})$. The proof exploits the self-similar structure of the sequence, where alternating arches arise whose frequency combinatorics are governed by the Catalan numbers. A complementary analysis of the arch amplitudes, conditional on two minimal conjectural properties, refines the asymptotic formula to $\limsup_{n\to\infty} |\widetilde{Q}(n)/n - 1/2| \sqrt{\log_2 n} = 1/(3\sqrt{2π})$. Numerical experiments suggest the conjecture $Q(n) - \widetilde{Q}(n) = O(n/\sqrt{\log n})$, indicating that $\widetilde{Q}$ may serve as a tractable proxy for Q. This experimental direction will be investigated elsewhere.

Scientific publishing systematically filters out negative results. We argue that this long-standing asymmetry has become an urgent problem in the era of large language models, which inherit the positive bias of the literature they are trained on, face an impending shortage of high-quality training data, and are increasingly deployed as both research tools and peer reviewers. We analyze three ways in which LLMs have changed the value of failure data and show that the systematic absence of such data degrades their utility as research tools, training data consumers, and peer reviewers alike. We outline experimental protocols to validate these claims and discuss the structural conditions under which a failure-inclusive publishing culture could emerge.

The VASCO project has identified over 100,000 sub-second optical transients on photographic plates from the First Palomar Observatory Sky Survey (1949-1957), all predating artificial satellites. Cann (2026a) established that transient detection rates are dose-dependently suppressed during geomagnetic storms (Z = -3.391, p = 0.0007), ruling out emulsion defects and confirming the transients as real, magnetospherically coupled phenomena. Villarroel et al. (2022) constrained the source altitude to ~42,000 km (geosynchronous orbit) through an Earth-shadow deficit. This paper presents two results. First, a pre-registered empirical test reveals the full temporal recovery profile: transient rates remain suppressed at 55% of baseline during days 7-21 post-storm, then rise to 309% of baseline during days 25-45 (p = 0.00066, Wilcoxon rank-sum; all robustness checks significant). Combined with the dose-response staircase, the overall significance reaches 3.6-4.7 sigma (Fisher's method, range reflecting sensitivity to the independence assumption). Second, we propose a candidate physical mechanism: storm-enhanced electromagnetic trapping of charged micrometeoroid dust at L ~ 6.6, followed by aggregation of icy cometary grains under restored cold plasmaspheric conditions. A flux dilution analysis demonstrates that specular reflection from a partially reflective icy aggregate only 1-4 m in diameter suffices to produce the observed plate magnitude at 42,000 km. This mechanism connects the VASCO transients to independently observed magnetospheric dust swarms correlated with geomagnetic activity (Sommer 2024) and explains the extinction of the transient population following the onset of the space age. Multi-site replication is required to confirm these results.

User models for recommender systems (RecSys) typically assume stable preferences, similarity-based relevance, and session-bounded interactions -- assumptions derived from high-volume consumer contexts. This paper investigates these assumptions for humanities scholars working with digital archives. Following a human-centered design approach, we conducted focus groups and analyzed interview data from 18 researchers. Our analysis identifies four dimensions where scholarly information-seeking diverges from common RecSys user modeling: (1) context volatility -- preferences shift with research tasks and domain expertise; (2) epistemic trust -- relevance depends on verifiable provenance; (3) contrastive seeking -- researchers seek items that challenge their current direction; and (4) strand continuity -- research spans long-term threads rather than discrete sessions. We discuss implications for user modeling and outline how these dimensions relate to collaborative filtering, content-based, and session-based recommendation. We propose these dimensions as a diagnostic framework applicable beyond archives to similar application domains where typical user modeling assumptions may not hold.

Scientific research is a key input into technological innovation, yet not all scientific knowledge is equally mobilized in patents. This paper examines how different scientific publishing models shape both the selection of scientific publications cited in patents and their cognitive alignment with patented technologies. Using large-scale data on non-patent references linking patents to scientific publications, combined with metadata from OpenAlex, we compare the Open Access (OA) structure of patent-cited science to that of the scientific literature. We then assess cognitive alignment using semantic similarity between patent abstracts and the abstracts of cited publications, distinguishing between citations appearing in the front section of patents and those embedded in the body of patent texts. We find that patent citations disproportionately draw on publications disseminated through highly visible and institutionally established publishing channels, particularly hybrid and bronze OA models, indicating strong selection effects. However, this dominance in citation counts does not translate into stronger cognitive alignment with patented technologies. On the contrary, publications in fully OA journals (gold and diamond OA) exhibit equal or higher semantic proximity, especially when cited in the body of patents. These results suggest that the contribution of OA to innovation depends less on access alone than on how different publishing models are embedded in information infrastructures that shape the visibility, discoverability, and use of scientific knowledge.

As exposure to electromagnetic waves becomes increasingly widespread, it is important to quantify how incident fields couple into biological tissue and where absorbed energy is deposited. This work presents an analytical, physics based framework derived from Maxwell's equations to model the propagation of a normally incident electromagnetic plane wave within homogeneous, lossy dielectric biological tissues. Closed-form expressions for the electric and magnetic fields are derived, enabling the determination of frequency-dependent power reflectance and transmittance at the air-tissue interface, as well as the power absorption coefficient and penetration depth within the medium. Using complex relative permittivity data from the literature, we examine six tissue types across a broad frequency range (1 MHz to 100 GHz). The results demonstrate that higher water content significantly increases dielectric loss and reduces penetration depth. Conversely, low-water tissues (e.g., non-infiltrated fat) exhibit lower attenuation and deeper penetration. Frequency is shown to be a dominant driver of this behavior, with higher frequencies shifting the power budget from reflection-limited coupling toward highly superficial absorption. These findings provide a foundation basis for exposure assessments and the design of emerging electromagnetic technologies.

Since its humble origins, humans have left imprints on the face of the planet. From the profound transformation unleashed by the Neolithic Revolution, about 12000 years ago, till the present, humans have reshaped the planet significantly. From the second half of the XX century, the impact on the atmosphere, biosphere, cryosphere, hydrosphere and upper lithosphere is so overwhelming that a new geological age, the Anthropocene, was proposed to consider the extent of these transformations. However, despite the ubiquitous nature of the changes in course, the International Union of Geological Sciences rejected in March 2024 formalizing the Anthropocene as a new geological epoch. This controversial decision implies that geologists are not quite convinced that human activities have reached the level of an encompassing new geological age. Nevertheless, it is beyond any doubt that there is no single spot on the planet where the signs of the transformations ensued by the human activities are not felt. Furthermore, the interconnection of the human activities has reached a level of entanglement that it makes the Anthropocene an inescapable feature of our present and immediate future. Thus, more important than framing our present condition in a way that it can be recognised by geologists in the future, is the understanding that by its very nature, the Anthropocene is a condition that is continuously being reshaped to the point that we should instead regard our time as a Transientocene, a time of significant and multidimensional transformations.

Four-dimensional scanning transmission electron microscopy (4D-STEM) generates multi-gigabyte datasets, creating a growing mismatch between acquisition rates and practical storage, transfer, and interactive visualization capabilities. We systematically benchmark 13 lossless compression implementations across 5 representative datasets (8~MiB to 8~GiB, 49.5--92.8\% sparsity), with 10 independent runs per method. HDF5 provides built-in gzip compression, of which gzip-9 typically achieves the highest compression ratio but is slow. We therefore evaluate widely available alternatives (via hdf5plugin), including the Blosc family. As a representative comparison, blosc\_zstd achieves compression comparable to gzip-9 (mean 13.5$\times$ vs 12.3$\times$) while compressing 19--69$\times$ faster and reading 1.9--2.6$\times$ faster across datasets. Compression ratios are deterministic, and timing measurements are highly reproducible (CV $<$2\%). Compression performance follows a power law with sparsity ($R^2 = 0.99$), ranging from 5$\times$ for moderately sparse data to 35$\times$ for highly sparse data. We identify six top-performing implementations optimized for different use cases and demonstrate that 4D-STEM data can be routinely compressed by $>$10$\times$. While these results provide practical guidance for lossless compression selection, the broader conclusion is that lossless compression preserves measurements but does not by itself guarantee sustainable high-throughput workflows. As detector rates rise, data handling will increasingly require inference-driven representations -- i.e., deciding what must be preserved to support a scientific inference, rather than defaulting to storing fully dense raw measurements.