Einstein derived the expansion of space ever since the Big Bang started and introduced the possible cosmological constant Λ. The expansion of space and the present-day expansion rate H0, the Hubble constant, has been discovered by Hubble. Perlmutter discovered the positive value of Λ, and Zeldovich showed that Λ corresponds to the energy density uDE of space. Lamb and Retherford as well as Casimir provided evidence for the idea that uDE might be based on quanta, and Riess et al. provided evidence that H0 is an idealization. In this paper, using the hypothetico-deductive method with very founded hypotheses, these two pieces of evidence are confirmed in a very founded and precise manner. Thereby, neither a fit is executed, nor a postulate, nor an unfounded hypothesis is proposed.

Abstract In this work, the behavior, evolution, and expansion of the universe are investigated within a Lorentz-violating framework driven by Tsallis holographic dark energy. The cosmological extension is implemented through a spontaneously symmetry-breaking Bumblebee field, which is assumed to play a fundamental role in the dynamics of the universe. Estimates for key Lorentz-violating quantities are obtained, and the evolution of the Hubble parameter is analyzed from the early universe era to the present epoch. This formulation provides an alternative perspective on the Hubble tension.

The literature suggests that dark energy is responsible for the accelerating expansion of the universe due to its negative pressure, therefore, dark energy can be used as a possible option to prevent the gravitational collapse of compact objects into singularities. In this regard, there is a great possibility that dark energy can interact with the compact stellar matter configuration [Phys. Rev. D 103 (2021) 084042]. In this paper, we introduce a physically viable model for celestial compact stars made of isotropic baryonic matter and isotropic dark energy with Heintzmann’s ansatz [Z. Phys. 228 (1969) 489–493] in the context of Einstein’s gravity. Here, the density of dark energy is assumed to be proportional to the density of baryonic matter. The main focus of this paper is to see the effects of dark energy on the physical properties of the stars. We perform an in-depth analysis of the physical attributes of the model, such as metric function, density, pressure, mass–radius relation, compactness parameter, gravitational and surface redshifts, along with the energy conditions for three well-known compact stars. We analyze the equilibrium of the present model via the generalized Tolman–Oppenheimer–Volkoff equation and the stability with the help of the adiabatic index and Harrison–Zeldovich–Novikov’s static stability condition. Moreover, we estimate the solutions representing the maximum masses and the predicted surface radii from the [Formula: see text]–[Formula: see text] graph for different values of the coupling parameter [Formula: see text]. All the analyses ensure that the present model is nonsingular and physically viable by satisfying all the essential conditions.

The past decades have seen significant expansion of the nucleic acid space with the development of modified artificial base pairs based on natural nucleic acid analogues and even entirely new designed base pairs. This expansion has led to tremendous potential for applications such as site-specific labeling and structural investigations. However, natural nucleic acids rely strongly on hydrogen bonding as the attraction force, which has driven the development of artificial base pairs that adapt this mechanism. To achieve orthogonality with natural bases, unnatural base pairs with alternative attraction forces, such as hydrophobic interactions, have been developed which however can perturb duplex structures. Our work introduces a completely newly designed artificial base pair that leverages halogen bonding (R-Hal···) as a directional hydrogen bonding-like interaction. We report computational studies that led to the development of this novel unnatural base pair and its synthesis. Our results demonstrate the successful acceptance of a bulky iodinated nucleoside triphosphate by KlenTaq DNA polymerase, enabling the selective enzymatic synthesis of a DNA strand containing the dIIPO-doIPP base pair. Our work presents the first demonstration of a novel base pair with halogen bonding potential that is enzymatically incorporated into DNA.

A bstract Gravitational waves (GWs) provide a powerful probe of the early universe due to their ability to free-stream across cosmic history. We study GW production in a compelling scenario where a rotating axion(-like) field becomes relevant for a brief period in the early universe before transitioning into a kination fluid and rapidly dissipating its energy through cosmic expansion. During this short epoch, the curvature perturbation can be predominantly sourced by the rotating axion and may significantly exceed the adiabatic component. Moreover, axion field perturbations grow on superhorizon scales during this phase. These effects can generate a strong stochastic background of induced GWs. This GW background also exhibits a pronounced large-scale anisotropy inherited from the axion fluctuations, serving as a distinctive signature of the scenario. Importantly, the transient nature of axion relevance enables this scenario to evade stringent bounds on large-scale perturbations. We analyze various observational constraints and find that both the amplitude and anisotropy of the resulting GW signal could be accessible to future detectors.

Purpose Relative density (RD) is a key quality indicator in laser-based powder bed fusion (L-PBF), linked to microstructure, mechanical properties and performance. This study aims to improve the prediction of RD by integrating a wider set of continuous and categorical inputs, capturing multifactorial interactions beyond the process parameters. Design/methodology/approach A data set of 1,579 samples was compiled from 85 peer-reviewed studies, covering multiple alloys, atmospheres, geometries and measurement methods. Exploratory data analysis combined mutual information and correlation metrics to assess feature relevance. K-means clustering segmented the data into homogeneous groups. Within each cluster, ensemble learning models were optimized via grid search and metaheuristics, with performance validated against literature and experimental data. Findings The cluster-driven framework achieved high predictive accuracy (R2= 0.94) across alloys and process ranges. Clustering improved generalization, especially in low-density regimes. Feature relevance varied by cluster: powder D50, geometric factor and laser power consistently ranked highest. Gradient boosting performed best in some clusters, while weighted-sum and voting ensembles provided the most balanced accuracy. SHAP analysis revealed complex, nonlinear interactions among geometric and process parameters. Originality/value This work introduces several novel contributions to the prediction of RD in L-PBF: the expansion of the input feature space to include underused variables such as material, shielding atmosphere, geometric descriptors and a newly defined shape factor; the use of a cluster-specific modeling strategy (“cluster-then-model”) that tailors regressors to data subgroups based on process-response similarity; and the integration of dual-ensemble optimization with explainability methods, resulting in a robust, transferable and interpretable framework for process performance prediction in metal additive manufacturing.

Hubble constant tension, together with the recent indications of dynamical dark energy proposed from the Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) measurements, poses significant challenges for the standard cosmological model. We investigate the possible redshift evolution of dark energy and the Hubble constant through a data-driven approach, and assess whether such evolution can alleviate the Hubble constant tension. We perform a model-independent reconstruction of the dark-energy equation of state w(z), jointly with an evolving Hubble constant H_0(z). The analysis combines the DESI DR2 BAO dataset with multiple Type Ia supernova samples and evaluates the statistical preference for the reconstructed model using Bayesian evidence. The reconstructed w(z) varies with redshift and exhibits two potential phantom crossings at z and z = 5.04 (8.53). The results remain stable under different prior choices and dataset combinations. Meanwhile, H_0 decreases continually from local to high redshift, alleviating the Hubble constant tension effectively. The joint w(z)-H_0(z) model is favored over the wCDM (ΛCDM) framework, with a logarithmic Bayes factor, łn B Our data-driven reconstructions suggest redshift evolution in both w(z) and H_0(z), offering a potential route to mitigate the Hubble constant tension. Future BAO measurements from Euclid and next-generation CMB experiments will provide critical tests of these results and bring deeper insights into the nature of dark energy and the evolution of cosmic expansion.

Abstract We investigate the gluon Wigner distributions at non-zero skewness using light-front wave functions (LFWFs) in the dressed quark model, where the target state is a quark dressed with a gluon in the leading-order Fock space expansion. Our analysis focuses on the configurations in which the gluon, the target, or both are transversely polarized. We derive analytical expressions for the Wigner distributions in the boost-invariant longitudinal space $(\sigma)$ for transversely polarized configurations and observe a diffraction-like oscillatory pattern in $\sigma$-space, analogous to that reported earlier for unpolarized and longitudinally polarized gluons. Content from this work may be used under the terms of the Creative Commons Attribution 3.0 licence. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI. Article funded by SCOAP3 and published under licence by Chinese Physical Society and the Institute of High Energy Physics of the Chinese Academy of Science and the Institute of Modern Physics of the Chinese Academy of Sciences and IOP Publishing Ltd.

A simple fourth-order propagator [Ture and Jang, J. Phys. Chem. A 128, 2871 (2024)] based on the Magnus expansion is extended to the Liouville space for both closed-system and Lindbladian open-system quantum dynamics. For both dynamics, commutator free versions of fourth-order propagators are provided as well. These propagators are then applied to the dynamics of a driven Λ-system, where Lindblad terms represent the effect of a photonic bath. For both dynamics, the accuracy of the rotating wave approximation (RWA) for the matter-radiation interaction is assessed. We confirmed reasonable performance of RWA for weak and resonant fields. However, small errors appear for moderate fields and substantial errors can be found for strong fields where coherent population trapping can still be expected. We also found that the presence of bath for open-system quantum dynamics consistently reduces the errors of the RWA. These results provide quantitative information on how the RWA breaks down beyond weak field or for non-resonant cases. Major results are benchmarked against results of our sixth-order ME-based propagator. We also provide numerical comparison of our algorithms with other fourth-order algorithms for the Λ-system. These confirm reasonable performance of our simple propagators and the improvement gained through commutator-free expressions.

We analyze DESI DR2 data with a model-independent method and find that: (a) the expansion of the universe may speed up with a confidence level more than 2.3 [Formula: see text] at redshift [Formula: see text]; (b) the expansion of the universe may speed down with a confidence level greater than 1.7 [Formula: see text] at redshift [Formula: see text]; (c) [Formula: see text] with confidence level exceeding 1.6 [Formula: see text] at redshift [Formula: see text].

Vision begins in the outer segment compartment of photoreceptor cells, which is constantly renewed through the addition of membrane material at its base and ingestion of mature membranes at its tip by the retinal pigment epithelium (RPE). The close apposition of outer segments to the RPE is believed to be critical for maintaining this renewal process. Yet, in several retinal diseases, expansion of the subretinal space separating photoreceptors from the RPE does not immediately impact photoreceptor functionality. Here, we analyzed outer segment function and renewal in the Adam9 knockout mouse characterized by a major expansion of the subretinal space. Surprisingly, photoreceptor-RPE separation affected neither the sensitivity of photoreceptor light-responses nor the normal rate of outer segment renewal in this mouse prior to the onset of photoreceptor degeneration. The latter is achieved through the formation of elongated RPE "pseudopods" extending across the enlarged subretinal space to ingest outer segment tips. This work suggests that pseudopod formation may underlie the persistence of photoreceptor function in human diseases accompanied by photoreceptor-RPE separation, such as vitelliform macular dystrophy or age-related macular degeneration associated with subretinal drusenoid deposits.

Correcting the scaphocephalic skull shape caused by sagittal craniosynostosis has likely produced a wider variety of treatment approaches than any other single-suture synostosis. Considering that the goals of surgery are to enlarge the intracranial space to facilitate cerebral blood flow and to normalize appearance in all 3 dimensions, the ideal repair should safely and effectively accomplish both objectives. A technical variation on a remodeling procedure is presented, based on the creation of a sagittal bandeau, which provides the foundation for a correction that not only widens the posterior biparietal distance but also remedies the posterior reduction in skull height, resulting in both an expansion of the intracranial space and a normalization of appearance.

ABSTRACT Introduction AI has tremendous potential to reduce time and costs taken to discover and develop new medical entities. As technology evolves, it is essential to learn from successes and failures to realign expectations for scientists, stakeholders and investors. Areas covered The authors discuss the challenges associated with the traditional reductionist approach to drug discovery which relies on incomplete data for target validation and, specifically for small molecules, the expanse of chemical space providing potential candidates. The promise of AI is illustrated by both early success and failure stories. Lessons learned are provided at levels of realism, adoption and integration of AI within current Research and Development (R&D) organizational structures. Expert opinion The first decade of AI adoption in Big BioPharma has been characterized by genuine breakthroughs and sobering realities. While AI has delivered notable accelerations in hit identification and early-stage design, it has yet to fundamentally alter the success rates of late-stage clinical trials. The industry has learned that AI is neither a silver bullet nor a passing fad, though a critical and evolving component of modern R&D. By consolidating lessons from early adoption, the next decade may see AI truly shift the innovation frontier in global pharmaceutical discovery.

Abstract The accelerated expansion of the Universe remains a fundamental challenge in cosmology, motivating model‐independent methods to reconstruct its expansion history without relying on specific dark energy models. Cosmography, which employs series expansions of cosmological observables around the present epoch, provides a powerful kinematic framework rooted in the cosmological principle. However, standard Taylor expansions suffer from limited convergence at high redshift, prompting the exploration of alternative expansions. In this work, logarithmic polynomial cosmography is investigated, which expands observables in powers of the logarithm of redshift, thereby enhancing convergence over a broad redshift range while maintaining physical insight. The logarithmic polynomial parameters are constrained using recent datasets, including gravitational‐wave standard sirens, DESI DR2, cosmic chronometers , and multiple Type Ia supernova compilations (DES‐SN5YR, Union3, Pantheon+SH0ES). The analysis demonstrates the efficacy of the logarithmic approach in accurately modeling the cosmic expansion history, providing an interpretable alternative to traditional cosmographic techniques.

Bright sirens, i.e. gravitational-wave detections of compact binary mergers with electromagnetic counterparts, provide a self-calibrated distance-redshift relation and are therefore powerful probes of cosmic expansion. Using the CosmoDC2_BCO catalog, we forecast cosmological constraints from current (LVK) and next-generation (ET, CE) detector networks, in combination with a Roman-like Type Ia supernova sample. We find that third-generation networks reach sub-percent precision on the Hubble constant within a few years, achieving 0.2% after a decade with CE+ET+LVK, while LVK remains limited to the 6% level. The LVK fifth observing run may shed light on the H 0 tension only if the inferred value falls outside the range spanned by the Planck and SH0ES determinations, which currently achieve far higher precisions. Supernovae do not directly tighten H 0 but stabilize its inference through parameter correlations and enable an absolute calibration of the supernova magnitude MB . In dynamical dark-energy models, the joint analysis of Roman supernovae and bright sirens yields a Figure of Merit of 25 for ET+LVK and 76 for CE+ET+LVK, to be compared with the state-of-the-art DESI DR2 BAO plus DESY5 supernovae value of 56. Sky-localization thresholds of ΔΩ < 50 deg2, or even ΔΩ < 10 deg2, entail only mild penalties, suggesting efficient follow-up strategies. These results establish third-generation GW+EM observations, especially when combined with Roman supernovae, as a cornerstone for precision cosmology in the next decade.

High-connectivity polycube-maps: Solvable space expansion through validity-augmented topological conditions

Background: The relationship between nature and society is an ecological unity that is mutually formed. However, in the historical premise of capitalism, man is separated from nature and placed in relations of production that make nature an object of exploitation for the accumulation of capital. In Indonesia, the expansion of extractive industries, particularly mining, does contribute to national economic growth, but at the same time results in structural land grabbing, especially in indigenous territories. This study aims to examine the practice of mining as a manifestation of the capitalist system that perpetuates agrarian injustice, deprivation of land rights, and the marginalization of Indigenous Peoples. Methods: The research uses qualitative method through literature review and comparative-descriptive case study with socio-legal approach to examine the relationship between socio-ecological reality due to mining and agrarian legal framework. Data were obtained from Scopus and Web of Science indexed journals, ecological perspective books, and NGO and institutional reports. The analysis was carried out using Marxist ecological theory, which views capitalism as treating nature as an unlimited resource that can be exploited, as well as agrarian law theory, which asserts that the earth, water, and natural resources must be controlled by the state for the greatest prosperity of the people. Findings: The findings show that the hegemony of the mining industry in Papua, North Maluku, NTT, and Sumatra is causing the loss of indigenous peoples' living space, resulting in increased poverty and decreased health quality, as well as triggering ecological damage such as deforestation and river pollution. This condition is consistent with the Marxist ecological perspective that capitalism encourages degrarianization and creates ecological disharmony through the expansion of extractive industrial production space. In addition, formal agrarian law often fails to protect indigenous peoples' rights to land as living space. Conclusion: In conclusion, capitalism transforms nature from a living space into an object of production, while the role of the state that facilitates extractive industries reinforces structural inequality and ignores the mandate of Agrarian Justice in Article 2 point 3 of the 1960 Constitution. Novelty/Originality of this article: The novelty of this study lies in integrating Marxist ecological theory with agrarian law analysis to reveal how capitalism and state-facilitated mining perpetuate indigenous dispossession and ecological degradation in Indonesia.

The standard model of cosmology claims that the Universe is expanding, driven by events arising out of the Big Bang. However, direct evidence for expansion faster than the Hubble shift (i.e. sub-luminal expansion) is lacking on local scales, and the assumption that the CMB is a relic of a hot origin is unnecessary. By applying Wien’s law to present-day cosmic conditions-namely, a Universe almost entirely empty, with a density of one particle per cubic meter - the observed 2.7 K temperature of the CMB emerges naturally, without invoking relic radiation. This paper argues that the CMB reflects present conditions, not ancient ones, and that cosmic expansion, if real, should be observable everywhere, not only at inaccessible distances. These considerations call for a fundamental re-evaluation of current cosmological assumptions.

In the twenty-first century, increasing studies suggest that magnetic devices are employed for technical innovation and solving clinical bottleneck problems. Magnetic surgery technology uses specially designed magnetic medical instruments or equipment to transform the "non-contact" magnetic force into a particular force. This could play a specific function in clinical diagnosis and treatment, such as vascular anastomosis, tissue compression, instrument anchoring, surgical navigation, space expansion, and controlled tracing. According to different application methods and principles, magnetic surgery technology can be divided into the following five core categories: magnetic compression technique (MCT), magnetic anchor technology (MAT), magnetic navigation technology (MNT), magnetic levitation technology (MLT), and magnetic tracer technology (MTT). Some of these technologies present great superiority and tend to replace traditional approaches, which are widely used in multiple diseases, including digestive, gynecological, breast, and urinary. From this perspective of multidisciplinary, magnetism could be utilized in clinical practice well, and magnetic surgery has tremendous potential in clinical treatment. The application of magnetic surgery makes operation greatly simplified and the postoperative complications reduced. Meanwhile, the biological security of magnetic surgery is assessed, and a uniform standard needs to be established. Despite some challenges that still exist in the development of magnetic surgery, it is necessary to investigate and explore more novel technologies, which bring clinical benefits to patients.

The discrepancy between early and late-Universe measurements of the Hubble constant, commonly referred to as the Hubble tension, remains one of the most significant open problems in modern cosmology. Strongly-lensed Type Ia supernovae provide a promising and independent probe of the cosmic expansion rate through time-delay cosmography, but their practical application is hindered by microlensing distortions and limited observational cadence. In this work, we present a machine-learning–based method for estimating time delays in microlensed Type Ia supernova light curves. Using realistically simulated lensed supernova datasets, we train and evaluate a Random Forest regression model to recover time delays between unresolved image pairs. We show that data-driven approaches can mitigate microlensing-induced biases relative to traditional cross-correlation methods and recover delays with improved robustness under challenging observational conditions. While this study does not perform a full cosmological inference, the results demonstrate the potential role of machine-learning techniques in future time-delay cosmography pipelines aimed at addressing the Hubble constant tension.