Browse Articles

Structural basis of menaquinone reduction by succinate dehydrogenase from Chloroflexus aurantiacus

Integrated neural network and metaheuristic algorithms for balancing electrical performance and thermal safety in PEMFC design

Correction: AVO reflectivity and pre-stack seismic impedance inversion for gas sand channel detection at South Abu El Naga Field, Onshore Nile Delta, Egypt

Buried deep freshwater reserves beneath salinity-stressed coastal Bangladesh

Abstract Aquifer overexploitation and saltwater intrusion threaten freshwater resources in coastal regions worldwide. In Bangladesh, arsenic contamination further reduces shallow freshwater availability. In the coastal zone, much of the shallow groundwater is saline, while the availability of deeper fresh groundwater remains poorly understood. Here, we utilize deep-sensing magnetotelluric soundings to image contrasts in electrical resistivity between fresh and saline groundwater along the Pusur River in the Ganges-Brahmaputra Delta. Our data reveal two distinct deep freshwater bodies separated by a high-salinity zone. We propose that these aquifers formed during the Last Glacial Maximum sea-level lowstand and are protected by overlying fine-grained sediments, whereas the Ganges paleovalley incision, followed by marine transgression and deposition, created the saline gap. Our work maps potential resources for this water-stressed region and suggests that the interplay between past sea-level cycles, sedimentation, and hydrogeological processes demonstrated here may also control the distribution of fresh groundwater in other deltas.

AiM: urban air quality forecasting with grid-embedded recurrent MLP model

Large language models versus classical machine learning performance in COVID-19 mortality prediction using high-dimensional tabular data

Electrically tunable ultrafast dynamics and interactions of hybrid excitons in a 2D semiconductor bilayer

Investigation of enhanced thermoelectric response in the extended Lieb lattice using the Hubbard model

Alterations in neuroinflammatory and neurodegenerative biomarkers among long-term residents of a critically polluted area: a cross-sectional comparative study

Porous organic cage mixed matrix membranes for efficient enantioseparation

Comparison of in vitro migration assays evaluating nintedanib’s migration inhibitory effects on melanoma cells

National assessment of transit electrification in Canada: infrastructure costs, energy demand, and greenhouse gas reduction potential

Audio long read: Faulty mitochondria cause deadly diseases — fixing them is about to get a lot easier

Cathepsin L as a dual-target to mitigate muscle wasting while enhancing anti-tumor efficacy of anti-PD-L1

5,7,4'-trimethoxyflavanone from Bauhinia variegata exerts anti‑inflammatory and protective actions in LPS‑challenged rat intestine

Noradrenergic modulation of pheromone-induced odor learning and brain activation in newborn rabbits

Quadrature squeezing in a nanophotonic microresonator

Abstract Squeezed states of light are essential for emerging quantum technology in metrology and information processing. Chip-integrated photonics offers a route to scalable and efficient squeezed light generation, however, parasitic nonlinear processes and optical losses remain significant challenges. Here, we demonstrate single-mode quadrature squeezing in a photonic crystal microresonator via degenerate dual-pump spontaneous four-wave mixing. Implemented in a scalable, low-loss silicon-nitride photonic-chip platform, the microresonator features a tailored nano-corrugation that modifies its resonances to suppress parasitic nonlinear processes. In this way, we achieve an estimated 7.8 dB of on-chip squeezing in the bus waveguide, with potential for further improvement. These results open a promising pathway toward integrated squeezed light sources for quantum-enhanced interferometry, Gaussian boson sampling, coherent Ising machines, and universal quantum computing.

Investigation on failure mechanism and mechanical response of layered rocks based on AE monitoring and 3D numerical simulation

Suramin protects against chronic stress-induced neurobehavioral deficits via cGAS–STING/NF-κB suppression

Quantum speedup for nonreversible Markov chains

Abstract Quantum algorithms can potentially solve a handful of problems more efficiently than their classical counterparts. In that context, it has been discussed that Markov chains problems could be solved significantly faster using quantum computing. Indeed, previous work suggests that quantum computers could accelerate sampling from the stationary distribution of reversible Markov chains. However, in practice, certain physical processes of interest are nonreversible in the probabilistic sense and reversible Markov chains can sometimes be replaced by more efficient nonreversible chains targeting the same stationary distribution. This study constructs Markov chain reversibilizations and develops quantum algorithmic techniques to accelerate nonreversible processes. Such an up-to-exponential quantum speedup goes beyond the predicted quadratic quantum acceleration for reversible chains and is likely to have a decisive impact on many applications ranging from statistics and machine learning to computational modeling in physics, chemistry, biology and finance.