Browse Articles

Magma convection favors ephemeral melt-rich bodies within mushy reservoirs

Abstract Magma convection is a mechanism that greatly enhances heat transfer from mobilizable, crystal-poor magma bodies to the surrounding immobile, crystal-rich mush reservoir of Earth’s igneous systems. As most of these systems are geophysically shown to be mush-dominated, magma convection is often omitted from thermo-kinetic models, and its role in magma evolution and eruptibility remains underexplored. Here we present 2-D numerical thermal modelling that parameterizes magma convection through a Nusselt-number approach that describes the local enhancement of heat transfers, and examine its effects in the axial mush zone of fast-spreading mid-ocean ridges. We demonstrate that magma convection, while not affecting the overall thermal regime of the mushy reservoir, significantly reduces the lifespan of individual pockets of eruptible melt to <2 years, which is two orders of magnitude shorter than in simulations without convection. Our models also show that magma convection could promote mush reheating and unlocking, potentially participating to the geochemical homogenization of heterogeneous melts extracted from the mantle. Predicted fluctuations in the occurrence and persistence of magma bodies provide insights into their highly transient nature, enhancing our ability to interpret geophysical snapshots of magma-mush systems in oceanic settings, and in other igneous systems.

Improved particle swarm optimization enables robust trajectory tracking of nonlinear robotic manipulators under external disturbances

Time-Temperature Superposition Principle Serving as a Design Tool for Tailored MOF-Crystal Glass Composite Membranes

Biofilm formation by Histophilus somni on 3D bovine respiratory tissue cultures

Improving economic impact assessment of climate change with machine learning

Deep CNN chaotic key generator for multi-parameter elliptic curves over cybersecurity image encryption application

Severe droughts in Senegal are linked to increased family reunification at migration destinations in Europe

Sea of Okhotsk warming impacts adult return abundance of southwestern marginal Chum salmon populations over four decades

Universal graph-based identifiers of chemical structures for linking large material databases

Aggregating XAI-based explanations to identify spectral–spatial patterns in CNN-based resting-state EEG classification

Abstract Convolutional neural networks (CNNs) achieve high performance in electroencephalographic (EEG) classification tasks; however, their decision-making mechanisms remain difficult to interpret. Explainable artificial intelligence (XAI) methods are typically applied to provide insight into individual model decisions, yet such explanations do not reveal the overall structure of the patterns learned by the network. In this study, we hypothesize that, in EEG analysis, XAI can serve a deeper role: when appropriately applied, it can expose the general patterns learned by a trained CNN, thereby transforming it from a purely predictive model into a framework capable of revealing candidate discriminative structures that may form the basis for future neuroscientific hypotheses. This shift is enabled by structured aggregation of local explanations, through which instance-level insights are consolidated into cross-subject patterns. To implement this strategy, we employ averagedLIME, an extension of Local Interpretable Model-agnostic Explanations, which aggregates sample-level explanations into global class-level saliency maps. In trial-based EEG paradigms, such aggregation reinforces consistent patterns across samples analogously to event-related potentials averaging. In this study, we examine whether this strategy remains effective in a temporally unaligned resting-state setting, taking subject-independent classification of Alzheimer’s disease and cognitively normal controls as a case study. Four neural architectures are compared, with spectral CNN yielding the most robust performance (95.81% test accuracy). The best-performing model is subsequently analyzed using averagedLIME, while SHapley Additive exPlanations (SHAP) and Grad-CAM are employed as complementary explanation techniques. Quantitative analyses demonstrated that the averagedLIME patterns were stable across cross-validation folds, robust to perturbation parameter selection, reproducible on unseen test data, and strongly supported by independent SHAP explanations. When interpreted in conjunction with the averaged input representations, the resulting saliency maps reproduced established EEG slowing patterns in Alzheimer’s disease, while also highlighting localized spatial–spectral structures that were less apparent in the averaged EEG representations alone. These findings demonstrate that structured aggregation of CNN explanations enables extraction of stable cross-subject patterns from resting-state EEG and may reveal candidate discriminative structures that can motivate future neuroscientific hypotheses.

Simultaneous nanoscale imaging of local conductivity and chemical potential in a quantum Hall isospin ferromagnet

Abstract Quantum Hall isospin ferromagnetism in multilayer graphene offers a versatile playground for exploring flat band correlated physics, driven by the intricate coupling of spin, valley, orbital, and layer degrees of freedom. However, a nanoscale probe capable of simultaneously mapping local conductivity and chemical potential in these exotic phases has yet to be realized. Here, we introduce scanning conductivity and chemical potential microscopy (SCCM), a technique integrating scanning microwave impedance microscopy and Kelvin probe force microscopy. We demonstrate SCCM by probing the quantum Hall states and many-body Landau level energy spectrum in bilayer graphene. Applied to marginally twisted double bilayer graphene, SCCM then reveals a cascade of quantum Hall isospin ferromagnetic states with unexpected re-emergence behaviors. Significantly, experimental many-body Landau level energy spectrum further uncovers the intricate connections of these complex phenomena to inter-subband Landau level crossings and Landau level single-particle wavefunctions. These insights enable the construction of a comprehensive quantum Hall phase diagram. Our results demonstrate SCCM’s capability in decoding complex quantum phenomena, establishing it as a versatile nanoscale probe for electron correlation and topology.

Oral cancer in the era of precision medicine: molecular targets and technological innovations

Divergent effects of pathological α-synuclein truncations and mutations on phase separation

Development of prediction model for identification of drug-related problems in ovarian cancer patients

Mechanistic insights into modulation of productive substrate accessibility for efficient PET depolymerization

Enhanced urine refractive index sensing using a defect-engineered one-dimensional photonic crystal

Abstract In this work, a defect-engineered one-dimensional photonic crystal (1D PhC) sensor is proposed for high-resolution urine refractive-index detection. The structure consists of alternating TiO₂/MgF₂ layers forming Bragg mirrors with a central urine-filled defect cavity, where a sharp localized resonance is generated inside the photonic band gap. Numerical analysis based on the transfer matrix method shows a stable red shift of the defect mode as the urine refractive index increases from 1.333 to 1.360. The proposed sensor achieves a sensitivity of 388.57 nm/RIU with excellent linearity $$\:({R}^{2}=0.999989)$$ , together with an average FWHM of 0.0305 nm, an average Q-factor of $$\:2.55\times\:{10}^{4},$$ and an average FOM of $$\:1.32\times\:{10}^{2}RI{U}^{-1}$$ . A tolerance analysis under ± 2.5% thickness variation confirms that the resonance remains narrow and well defined, demonstrating good fabrication robustness. The results also highlight an important principle in photonic sensing: sensor performance should not be judged by sensitivity alone, but by a balanced combination of sensitivity, linewidth, resonance quality, figure of merit, and tolerance against structural deviations. These findings indicate that the proposed design is a promising candidate for practical urine-based biomedical sensing applications. A comparison with representative previously reported photonic-crystal sensing platforms further confirms the balanced overall performance of the proposed design.

Dynamic roughening of cities driven by multiplicative noise

Abstract The evolution of urban landscapes is rapidly altering the surface of our planet. Yet our understanding of the urbanization phenomenon remains far from complete. A fundamental challenge is to describe spatiotemporal changes in the built environment, both vertical and horizontal. In this work, we model global building-height dynamics as a zero-dimensional geometric Brownian motion (GBM): multiplicative noise produces stochastic fluctuations around a drift linked to economic growth. To account for intra-city correlations, we extend the GBM with spatial coupling, revealing how local interactions effectively mitigate noise-driven fluctuations and shape urban morphology. In the continuum limit, the spatial model reduces to the Kardar-Parisi-Zhang (KPZ) equation, and roughness exponents estimated from data fall within the KPZ range for most cities. Our results indicate that multiplicative noise, moderated by local coupling, governs the evolution of urban roughness, placing city growth within a well-established statistical-physics framework.

Optimising thermal performance in data centre server racks via a parametric layout configuration study

Abstract The rapid expansion of data centres has intensified the demand for low-threshold thermal optimisation strategies that avoid the high costs and operational disruptions associated with conventional infrastructure retrofits. This study focuses exclusively on in-rack server layout adjustments, specifically server power combination, arrangement methods, and air supply velocity. A dedicated experimental platform was constructed to represent a standard server rack environment, and physical single-factor and orthogonal experiments were conducted to evaluate cooling efficiency. Results indicate that: (1) Heat accumulates primarily at the rack top, producing pronounced temperature stratification. (2) The relative impact of the examined parameters on thermal performance ranks as follows: air supply velocity > power combination > arrangement method. (3) An optimal configuration—400 W × 10 servers positioned in the lower rack with an air supply velocity of 4 m/s—effectively alleviates heat imbalance and suppresses hot-spots. Compared with a non-optimised baseline, this configuration yielded measurable improvements in airflow utilisation and thermal uniformity. This work demonstrates that strategic in-rack layout adjustments alone can markedly enhance cooling performance without additional capital investment or infrastructure modifications, offering actionable guidance for data centre operators seeking practical, low-effort optimisation measures.

Recognising and mitigating LLM Pollution in online behavioural research

The coexistence of MAFLD increases fibrosis burden in patients with chronic hepatitis B

Abstract Despite the increasing recognition of metabolic dysfunction-associated fatty liver disease (MAFLD) and Chronic Hepatitis B (CHB), their interplay on liver fibrosis remains insufficiently elucidated. This study aimed to assess liver fibrosis in CHB patients with concurrent MAFLD. This cross-sectional study included 148 patients with a confirmed diagnosis of CHB infection, categorized according to the MAFLD standard criteria into CHB with MAFLD (CHB/+MAFLD) and without MAFLD (CHB/−MAFLD). Demographic, metabolic, and biochemical parameters were analyzed. Fibrosis was assessed non-invasively using vibration-controlled transient elastography (VCTE). A multivariate logistic regression identified independent predictors of significant fibrosis. Among the CHB cohort, 34.5% fulfilled MAFLD criteria. Using FIB-4 > 1.3 for significant fibrosis, the CHB/+MAFLD group exhibited a higher prevalence of significant fibrosis 45.1% vs. 23.7% in the CHB/−MAFLD group, with higher medians in the CHB/+MAFLD group (1.2 vs. 0.8, p  < 0.001). Using liver stiffness measurement (LSM), those with MAFLD had a higher prevalence of significant fibrosis (F ≥ 2; 37.3 vs. 16.5%, p 0.005) than in those without MAFLD (medians 6.1 and 5.6 kPa, respectively, p 0.03). In multivariate analysis, MAFLD independently increased the odds of significant fibrosis (4.48, 95% CI 1.29–15.6, p 0.019) in CHB patients. The interplay of MAFLD and CHB is associated with worsening liver injury and fibrosis progression. Given the high prevalence of MAFLD and negative impact, metabolic risk assessment should be incorporated into routine CHB care to reduce fibrosis and improve long-term liver outcomes.