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Abstract Network glass fracture occurs as a sequence of elementary events occurring at weak sites in the glass structure. Fracture is a highly complex process that occurs suddenly and without obvious structural or thermodynamic signs prior to the event’s occurrence. We show that a stress threshold value quantified by local mechanical probing highly correlates with nanoscale crack nucleation in a two-dimensional network glass. Subsequently, a neural network-based predictor, the local intelligent stress threshold indicator (LISTI), links the local stress threshold with the undeformed local structural topology. LISTI yields a reliable heatmap indicating soft spots that strongly correlate with the localized initiation and development of the fracture process. Finally, we show that LISTI can be used to find local zones prone to rearrangement in real-measured two-dimensional silica glass structures. 

Abstract Gamma-ray bursts (GRBs) are among the most energetic events in the Universe, driven by relativistic jets launched from black holes (BHs) formed during the collapse of massive stars or after the merger of two neutron stars. The jet power depends on the BH spin and the magnetic flux accreted onto it. In the standard thin disk model, jet power is limited by insufficient magnetic flux, even when the spin approaches maximum possible value. In contrast, the magnetically arrested disk (MAD) state limits jet energy by extracting significant angular momentum, braking BH rotation. We propose a unified model incorporating both standard thin disk and MAD states, identifying a universal curve for jet power per accretion rate as a function of the magnetic flux ratio, ${\Delta }_{\mathrm{eq}}={({\Phi }_{\mathrm{BH}}/{\Phi }_{\mathrm{MAD}})}_{\mathrm{eq}}$, at spin equilibrium. For long GRBs (lGRBs), the model predicts a maximum jet energy of ∼1.5% of the accretion energy, occurring at Δ_eq ∼ 0.4, where the BH equilibrium spin isa ∼ 0.5. Both long and short GRBs are unlikely to be produced by a MAD: for short GRBs, this requires an accreted mass orders of magnitude smaller than that available, while for lGRBs, the narrow progenitor mass distribution challenges the ability to produce the observed broad distribution of jet energies. This framework provides a consistent explanation for both standard and luminous GRBs, emphasizing the critical role of magnetic flux. Both long and short GRBs require magnetic flux distributions that peak around 10²⁷G cm². 

Abstract In 2023, the Pulsar Timing Array Collaborations announced the discovery of a gravitational wave background (GWB), predominantly attributed to supermassive black hole binary (SMBHB) mergers. However, the detected GWB is several times stronger than the default value expected from galactic observations at low and moderate redshifts. Recent findings by the James Webb Space Telescope have unveiled a substantial number of massive, high-redshift galaxies, suggesting more massive SMBHB mergers at these early epochs. Motivated by these findings, we propose an “early merger” model that complements the standard merger statistics by incorporating these early, massive galaxies. We compare the early and standard “late merger” models, which assume peak merger rates in the local Universe, and match both merger models to the currently detected GWB. Our analysis shows that the early merger model has a significantly lower detection probability for single binaries and predicts a ∼30% likelihood that the first detectable single source will be highly redshifted and remarkably massive with rapid frequency evolution. In contrast, the late merger model predicts a nearly monochromatic first source at low redshift. The future confirmation of an enhanced population of massive high-redshift galaxies and the detection of fast-evolving binaries would strongly support the early merger model, offering significant insights into the evolution of galaxies and SMBHs. 

In mammals, the circadian clock coordinates cell physiological processes including inflammation. Recent studies suggested a crosstalk between these two pathways. However, the mechanism of how inflammation affects the clock is not well understood. Here, we investigated the role of the proinflammatory transcription factor NF-κB in regulating clock function. Using a combination of genetic and pharmacological approaches, we show that perturbation of the canonical NF-κB subunit RELA in the human U2OS cellular model altered core clock gene expression. While RELA activation shortened period length and dampened amplitude, its inhibition lengthened period length and caused amplitude phenotypes. NF-κB perturbation also altered circadian rhythms in the master suprachiasmatic nucleus (SCN) clock and locomotor activity behavior under different light/dark conditions. We show that RELA, like the clock repressor CRY1, repressed the transcriptional activity of BMAL1/CLOCK at the circadian E-box cis-element. Biochemical and biophysical analysis showed that RELA binds to the transactivation domain of BMAL1. These data support a model in which NF-kB competes with CRY1 and coactivator CBP/p300 for BMAL1 binding to affect circadian transcription. This is further supported by chromatin immunoprecipitation analysis showing that binding of RELA, BMAL1 and CLOCK converges on the E-boxes of clock genes. Taken together, these data support a significant role for NF-κB in directly regulating the circadian clock and highlight mutual regulation between the circadian and inflammatory pathways. 

Transition from laminar to turbulent flow occurring over a smooth surface is a particularly important route to chaos in fluid dynamics. It often occurs via sporadic inception of spatially localized patches (spots) of turbulence that grow and merge downstream to become the fully turbulent boundary layer. A long-standing question has been whether these incipient spots already contain properties of high-Reynolds-number, developed turbulence. In this study, the question is posed for geometric scaling properties of the interface separating turbulence within the spots from the outer flow. For high-Reynolds-number turbulence, such interfaces are known to display fractal scaling laws with a dimension $D\approx 7/3$, where the 1/3 excess exponent above 2 (smooth surfaces) follows from Kolmogorov scaling of velocity fluctuations. The data used in this study are from a direct numerical simulation, and the spot boundaries (interfaces) are determined by using an unsupervised machine-learning method that can identify such interfaces without setting arbitrary thresholds. Wide separation between small and large scales during transition is provided by the large range of spot volumes, enabling accurate measurements of the volume–area fractal scaling exponent. Measurements show a dimension of $D=2.36\pm 0.03$over almost 5 decades of spot volume, i.e., trends fully consistent with high-Reynolds-number turbulence. Additional observations pertaining to the dependence on height above the surface are also presented. Results provide evidence that turbulent spots exhibit high-Reynolds-number fractal-scaling properties already during early transitional and nonisotropic stages of the flow evolution.