Energy transfer, cross-scale coupling, and dissipation in astrophysical plasmas remain fundamental unresolved problems. The velocity-shear-driven Kelvin-Helmholtz instability (KHI), ubiquitous in plasmas, is a key multiscale mechanism enabling plasma mixing, particle energization and the solar wind-magnetosphere coupling, making it a critical process for understanding how plasma flow energy is converted from large to kinetic scales. Although in situ observations and simulations have linked KHI to turbulence onset and plasma heating, the precise pathways of energy transfer remain unexplored. By leveraging high-resolution, multi-point Magnetospheric Multiscale (MMS) observations, we quantify, for the first time, KHI energy conversion channels via pressure-strain and diagnostics. Inside vortices, thermal-to-flow energy transfer dominates, coinciding with local non-thermal features. Notably, energy pathways seem influenced by vortex evolution: rolled-up vortices tend to convert flow energy into thermal energy, whereas early-stage vortices mostly convert thermal energy back into flow. This study provides the first energy-balance-based description of KHI dynamics and further reveals the complexity of velocity shear energy dissipation into heat.
This study presents a comprehensive statistical comparison of solar wind measurements between the OMNI database which contains data collected at L1 that is shifted to the bow shock nose, and near-Earth observations from MMS, Cluster, and THEMIS missions near the bow shock nose. Using a threshold-based classification methodology, the analysis encompasses approximately 353 days (MMS), 283 days (Cluster), and 125 days (THEMIS) of solar wind intervals. Bisector regression analysis reveals that the anti-sunward flow component demonstrates exceptional agreement across all missions with near-unity slopes and correlation coefficients of 0.92 for THEMIS and 0.97 for MMS and Cluster. However, perpendicular velocity components show progressively degraded performance: exhibits correlation coefficients of 0.63-0.77 with intercepts ranging from 21.57 km/s (MMS) to 47.49 km/s (THEMIS), while shows weaker correlations (0.42-0.72) with intercepts of 4.73-11.94 km/s. Ion density measurements reveal systematic mission-specific biases: MMS and THEMIS show ion density regression slopes below unity (0.59 and 0.54, respectively), while Cluster shows a slope above unity (1.14) compared to OMNI measurements. Magnetic field measurements show higher consistency, with near-unity slopes and correlation coefficients exceeding 0.84 for most components. The northward magnetic field component exhibits elevated variance ratios and reduced correlations across all missions, reaching 0.74 for THEMIS. These results quantify inherent uncertainties in cross-platform solar wind comparisons and assess the accuracy of time-shifted solar wind measurements in the OMNI database as proxies for actual near-Earth conditions, with implications for space weather applications, multispacecraft studies, and magnetohydrodynamic simulation validation requiring accurate upstream boundary conditions.
This study examines the relationship between far-right populism and rural areas in Italy through Lega (formerly Lega Nord). Drawing on literature and textual analysis of party materials and national press, it shows how rural spatial imaginaries constructed by the party shaped its identity and messaging over three decades. These imaginaries intersect with discourses of spatial justice, highlighting the dynamic and evolving nature of Lega's engagement with rurality amid leadership and ideological shifts. The paper also proposes a methodology for other populist parties, providing a framework to explore how spatial grievances are discursively produced and mobilised to consolidate rural support.
Livestock farmers in the United Kingdom must balance productivity and economic viability with numerous sustainability goals, particularly those related to achieving net zero and nature restoration. Resultantly, interest is growing among stakeholders in organic and regenerative agriculture as potential routes to a sustainable food future. To explore the sustainability of these systems, a systematic literature review, following the PRISMA protocol, was conducted comparing the management practices used on organic and regenerative cattle and sheep farms in the UK. The relationship between farming system and management practices on outcomes for animal health, animal welfare, productivity, finances and biodiversity were compared to conventional. A total of 45 papers were included, revealing a gap in research on regenerative ruminant systems (2 documents), compared with organic systems (43 documents). Research on organic farming demonstrates that, compared to conventional farming, financial performance is improved, productivity is decreased, and no difference is frequently observed on animal health and welfare, and biodiversity. In contrast, empirical research on regenerative livestock farms only assessed earthworm abundance, as a soil health indicator, indicating the need for studies examining a broader range of outcomes. Comparisons were not possible between farm types due to heterogeneous study designs, highlighting the need for standardised methodologies to produce comparable evidence on sustainable farming practices. Methods used in organic research may be valuable for the design of studies evaluating regenerative livestock systems. Key management practices undertaken on livestock farms included rotational grazing, use of multi-species pastures, crop-livestock integration and improving biodiversity via practices like hedgerow maintenance.
Fusion-based hyperspectral image super-resolution aims to fuse low-resolution hyperspectral images (LR-HSIs) and high-resolution multispectral images (HR-MSIs) to reconstruct high spatial and high spectral resolution images. Current methods typically apply direct fusion from the two modalities without effective supervision, leading to an incomplete perception of deep modality-complementary information and a limited understanding of inter-modality correlations. To address these issues, we propose a simple yet effective solution for unsupervised HMIF, revealing that modality decoupling is key to improving fusion performance. Specifically, we propose an end-to-end self-supervised Modality-Decoupled Spatial-Spectral Fusion (MossFuse) framework that decouples shared and complementary information across modalities and aggregates a concise representation of both LR-HSIs and HR-MSIs to reduce modality redundancy. Also, we introduce the subspace clustering loss as a clear guide to decouple modality-shared features from modality-complementary ones. Systematic experiments over multiple datasets demonstrate that our simple and effective approach consistently outperforms the existing HMIF methods while requiring considerably fewer parameters with reduced inference time. The source code is in MossFuse .
