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The interaction between various wave-like structures in screeching jets is considered via both experimental measurements and linear stability theory. Velocity snapshots of screeching jets are used to produce a reduced-order model of the screech cycle via proper orthogonal decomposition. Streamwise Fourier filtering is then applied to isolate the negative and positive wavenumber components, which for the waves of interest in this jet correspond to upstream- and downstream-travelling waves. A global stability analysis on an experimentally derived base flow is conducted, demonstrating a close match to the results obtained via experiment, indicating that the mechanisms considered here are well represented in a linear framework. In both the global stability analysis and the experimental decomposition, three distinct wave-like structures are evident; these waves are also solutions to the cylindrical vortex-sheet dispersion relation. One of the waves is the well-known downstream-travelling Kelvin–Helmholtz mode. Another is the upstream-travelling guided jet mode that has been a topic of recent discussion by a number of authors. The third component, with positive phase velocity, has not previously been identified in screeching jets. Via a local stability analysis, we provide evidence that this downstream-travelling wave is a duct-like mode similar to that recently identified in high-subsonic jets. We further demonstrate that both of the latter two waves are generated by the interaction between the Kelvin–Helmholtz wavepacket and the shock cells in the flow. Finally, we consider the periodic spatial modulation of the coherent velocity fluctuation evident in screeching jets, and show that this modulation can be at least partially explained by the superposition of the three wave-like structures, in addition to any possible modulation of the Kelvin–Helmholtz wavepacket by the shocks themselves.

A visible-light-driven Bi 2 WO 6 /CuS p-n heterojunction was fabricated using an easy solvothermal method. The Bi 2 WO 6 /CuS exhibited high photocatalytic activity in a mixed system containing rhodamine B (RhB), tetracycline hydrochloride (TCH), and Cr (VI) under natural conditions. Approximately 98.8% of the RhB (10 mg/L), 87.6% of the TCH (10 mg/L) and 95.1% of the Cr(VI) (15 mg/L) were simultaneously removed from a mixed solution within 105 min. The removal efficiencies of TCH and Cr(VI) increased by 12.9% and 20.4%, respectively, in the mixed solution, compared with the single solutions. This is mainly ascribed to the simultaneous consumption electrons and holes, which increases the amount of excited electrons/holes and enhances the separation efficiency of photogenerated electrons and holes. Bi 2 WO 6 /CuS can be applied over a wide pH range (2–6) with strong photocatalytic activity for RhB, TCH and Cr(VI). Coexisiting dissolved organic matter in the solution significantly promoted the removal of TCH (from 74.7% to 87.2%) and Cr(VI) (from 75.7% to 99.9%) because it accelerated the separation of electrons and holes by consuming holes as an electron acceptor. Removal mechanisms of RhB, TCH, and Cr(VI) were proposed, Bi 2 WO 6 /CuS was formed into a p-n heterojunction to efficiently separate and transfer photoelectrons and holes so as to drive photocatalytic reactions. Specifically, when reducing pollutants (e.g., TCH) and oxidizing pollutants (e.g., Cr(VI)) coexist in wastewater, the p-n heterojunction in Bi 2 WO 6 /CuS acts as a “bridge” to shorten the electron transport and thus simultaneously increase the removal efficiencies of both types of pollutants.

Despite the increasing Siberian river discharge, the sensitivity of streamflow to climate forcing/permafrost thawing is poorly quantified. Based on the Budyko framework and superposition principles, we detected and attributed the changes in streamflow regimes for the three great Siberian rivers (Ob, Yenisei, and Lena) during 1936–2019. Over the past 84 years, streamflow of Ob, Yenisei and Lena has increased by ∼7.7%, 7.4% and 22.0%, respectively. Intensified precipitation induced by a warming climate is a major contributor to increased annual streamflow. However, winter streamflow appears to be particularly sensitive to temperature. Whilst rising temperature can reduce streamflow via evapotranspiration, it can enhance groundwater discharge to rivers due to permafrost thawing. Currently, every 1 °C rise in temperature likely leads to 6.1%–10.5% increase in groundwater discharge, depending on the permafrost condition. For permafrost-developed basins, the contribution to increased streamflow from thawing permafrost will continue to increase in the context of global warming.

Photonics offers a transformative approach to artificial intelligence (AI) and neuromorphic computing by enabling low-latency, high-speed, and energy-efficient computations. However, conventional photonic tensor cores face significant challenges in constructing large-scale photonic neuromorphic networks. Here, we propose a fully integrated photonic tensor core, consisting of only two thin-film lithium niobate (TFLN) modulators, a III-V laser, and a charge-integration photoreceiver. Despite its simple architecture, it is capable of implementing an entire layer of a neural network with a computational speed of 120 GOPS, while also allowing flexible adjustment of the number of inputs (fan-in) and outputs (fan-out). Our tensor core supports rapid in-situ training with a weight update speed of 60 GHz. Furthermore, it successfully classifies (supervised learning) and clusters (unsupervised learning) 112  × 112-pixel images through in-situ training. To enable in-situ training for clustering AI tasks, we offer a solution for performing multiplications between two negative numbers. The authors showcase a photonic tensor core in TFLN platform that achieves a computational speed of 120 GOPS for neural networks, with capabilities of in-situ training that support exciting prospects of negative number multiplication. The tensor core can efficiently process 112  × 112-pixel images, potentially scaling up AI tasks and offering nanosecond latency without needing a digital processor.

Vegetation growth in drylands is highly constrained by water availability. How dryland vegetation responds to changes in precipitation and soil moisture in the context of a warming climate is not well understood. In this study, warm drylands in the temperate zone between 30 and 50° N, including North America (NA), the Mediterranean region (MD), Central Asia (CA), and East Asia (EA), were selected as the study area. After verifying the trends and anomalies of three kinds of leaf area index (LAI) datasets (GLASS LAI, GLEAM LAI, and GLOBAMAP LAI) in the study area, we mainly used the climate (GPCC precipitation and ERA5 temperature), GLEAM soil moisture, and GLASS LAI datasets from 1981 to 2018 to analyze the response of vegetation growth to changes in precipitation and soil moisture. The results of the three mutually validated LAI datasets show an overall greening of dryland vegetation with the same increasing trend of 0.002 per year in LAI over the past 38 years. LAI and precipitation exhibited a strong correlation in the eastern part of the NA drylands and the northeastern part of the EA drylands. LAI and soil moisture exhibited a strong correlation in the eastern part of the NA drylands, the eastern part of the MD drylands, the southern part of the CA drylands, and the northeastern part of the EA drylands. The results of this study will contribute to the understanding of vegetation dynamics and their response to changing water conditions in the Northern Hemisphere midlatitude drylands.

Biological visual systems have evolved to process natural scenes. A full understanding of visual cortical functions requires a comprehensive characterization of how neuronal populations in each visual area encode natural scenes. Here, we utilized widefield calcium imaging to record V4 cortical response to tens of thousands of natural images in male macaques. Using this large dataset, we developed a deep-learning digital twin of V4 that allowed us to map the natural image preferences of the neural population at 100-µm scale. This detailed map revealed a diverse set of functional domains in V4, each encoding distinct natural image features. We validated these model predictions using additional widefield imaging and single-cell resolution two-photon imaging. Feature attribution analysis revealed that these domains lie along a continuum from preferring spatially localized shape features to preferring spatially dispersed surface features. These results provide insights into the organizing principles that govern natural scene encoding in V4. How natural scenes are represented by the neuronal populations of a specific visual area such as V4 remain not fully understood. The authors produced a dataset of widefield calcium imaging of macaque V4 responses to a large set of natural images, and used deep learning techniques to elucidate how natural image features are encoded and topologically organized in V4.

Background and objectives: Despite the growing body of research on Parkinson’s disease (PD) and insomnia, comprehensive analysis of overall research trends remains limited. This study aims to evaluate these trends and identify research hot spots using bibliometric analysis.

The northern mid-high latitudes experience climate warming much faster than the global average. However, the difference in the temperature change rates between permafrost and non-permafrost zones remains unclear. In this study, we investigated the temporal changes in temperature means and extremes across the Siberian lowlands (<500 m) over the past six decades (1960–2019) using in situ observations and reanalysis data. The results show that permafrost zones (0.39 °C/decade) have warmed faster than non-permafrost zones (0.31 °C/decade). The minimum values of the daily maximum ( TXn ) and minimum ( TNn ) temperatures changed faster than their maximum values ( TXx, TNx ), suggesting that low minimum temperatures increase faster, as evidenced by the considerably higher warming rate in the cool season (October–April, 0.43 ± 0.10 °C/decade, n = 126) than that in the warm season (May–September, 0.25 ± 0.08 °C/decade, n = 119). The change rates of TXx and TNx in permafrost areas were 2–3 times greater than those in non-permafrost areas; however, over the last ten years, TXx and TNx in non-permafrost areas showed decreasing trends. Moreover, faster-warming permafrost regions do not exhibit a faster increase in surface net solar radiation than slower-warming non-permafrost regions. While our findings suggest that carbon emissions from thawing soils are likely a potential driver of rapid warming in permafrost-dominated regions, the potential feedback between ground thawing and climate warming in permafrost regions remains uncertain.

Stabilization of topological spin textures in layered magnets has the potential to drive the development of advanced low-dimensional spintronics devices. However, achieving reliable and flexible manipulation of the topological spin textures beyond skyrmion in a two-dimensional magnet system remains challenging. Here, we demonstrate the introduction of magnetic iron atoms between the van der Waals gap of a layered magnet, Fe 3 GaTe 2 , to modify local anisotropic magnetic interactions. Consequently, we present direct observations of the order-disorder skyrmion lattices transition. In addition, non-trivial topological solitons, such as skyrmioniums and skyrmion bags, are realized at room temperature. Our work highlights the influence of random spin control of non-trivial topological spin textures. Stabilizing non-trivial magnetic spin textures at room temperature remains challenging. Here, the authors propose introducing magnetic atoms into the van der Waals gap of 2D magnets Fe 3 GaTe 2 to stabilize the magnetic spin textures beyond skyrmion.

Free-space optical (FSO) communication provides high-capacity and secure links but is strongly impaired by atmospheric turbulence, which induces multi-scale irradiance fluctuations. Traditional approaches such as adaptive optics, multi-aperture and multiple-input multiple-output FSO schemes offer limited robustness under rapidly varying turbulence, while statistical fading models such as log-normal and Gamma–Gamma cannot represent multi-scale temporal correlations. This work proposes a hybrid deep learning framework that explicitly separates high-frequency scintillation and low-frequency power drift through a conditional variational autoencoder and a bidirectional long short-term memory dual-branch architecture with an adaptive gating mechanism. Trained on OptiSystem-generated datasets, the model accurately reconstructs irradiance distributions and temporal dynamics. For model-assisted signal compensation, it achieves an average 79% bit-error-rate (BER) reduction across all simulated scenarios compared with conventional thresholding and Gamma–Gamma maximum a posteriori detection. Transfer learning further enables efficient adaptation to new turbulence conditions with minimal retraining. Experimental validation shows that the compensated BER approaches near-zero, yielding significant improvement over traditional detection. These results demonstrate an effective and adaptive solution for turbulence-impaired FSO links.