Reading Museum

Anticipatory Action is increasingly put forward as a key approach to managing the emerging risks of climate change, by using forecasts to deliver vital resources to communities before disaster strikes. However, with climate change driving unprecedented weather extremes, how are anticipatory triggers, actions and implementation plans being designed to effectively prepare for and manage changing and emerging risks?In this research we identify examples of existing good practice, potential obstacles to progress, and ways in which weather and climate science can be better harnessed to strengthen anticipatory action as a climate adaptation tool. We use a mixed-methods approach, combining literature reviews, key informant interviews and stakeholder workshops. We find that while anticipatory action programming is usually informed by analysis of past events, there are emerging examples of good practice. These include addressing changing patterns of risk, undertaking scenario planning and simulation exercises, adapting triggers to account for upward trends in event frequency, and working to address the dangers of emerging risks such as heat waves and glacial lake outburst floods. However, in complex settings, for example in 'temporary' displacement camps, there is a need for longer-term thinking supported by integrated anticipatory action and resilience programming.

We prove the Zilber–Pink conjecture for curves in Y(1)n whose Zariski closure in (P1)n passes through the point (∞,…,∞), going beyond the asymmetry condition of Habegger and Pila. Our proof is based on a height bound following André’s G-functions method. The principal novelty is that we exploit relations between evaluations of G-functions at unboundedly many non-Archimedean places.

This paper presents a novel Temporal-Spatial Attention Network (TSAN) architecture for detecting Denial of Service (DoS) attacks in network traffic. By leveraging both temporal and spatial features of network traffic, our approach captures complex traffic patterns and anomalies that traditional methods might miss. The TSAN model incorporates transformer-based temporal encoding, convolutional spatial encoding, and a cross-attention mechanism to fuse these complementary feature spaces. Additionally, we employ multi-task learning with auxiliary tasks to enhance the model’s robustness. Experimental results on the NSL-KDD dataset demonstrate that our approach achieves superior performance compared to state-of-the-art methods, with high accuracy and AUC scores for DoS attack detection. The proposed architecture offers a balance between detection accuracy and computational efficiency, making it suitable for real-time network security applications

Abstract To evaluate numerical weather predictions (NWP) using observations, the horizontal, vertical and temporal resolution characteristics need to be considered to ensure consistency. Here, large‐aperture scintillometry (LAS) is used to derive turbulent sensible heat fluxes ( Q H ), as the source area extents are comparable to NWP with horizontal grid box resolutions of order 1 km. We demonstrate our methodology using LAS observations undertaken in central London and the Met Office's operational forecast model for the United Kingdom (UKV). In the horizontal, we ensure consistency between the LAS source area locations and extent, as they vary with wind direction and stability, and therefore the NWP grid boxes that should be selected for comparison. In the vertical, the appropriate model level needs to be selected relative to the changing effective measurement height ( z f ) of the observations. The LAS fluxes time and space averaging allows Q H to be obtained for time periods similar to the model timestep (e.g., 1 min) but the UKV does not explicitly represent higher‐frequency turbulent motions. During two spring days, three to five UKV grid boxes (1.5 km resolution) fall within the LAS source area (60%). However, with the model land cover data lacking realistic spatial variability, modelled effective surface Q H spatial variability is small, leaving a central grid box on the LAS path representative. Larger differences occur between modelled Q H at the surface and closest to z f , despite both theoretically being within the inertial sublayer. The modelled Q H has better agreement with observed fluxes when averaged over long periods (10+ min cf . 1 min). As LAS‐derived Q H has larger source areas than eddy covariance (EC) measurements, there are benefits to assessing NWP model performance with LAS. With multiple paths, spatial variability in response to changes in surface cover could be assessed at shorter times scales than possible with EC.

In a changing world, long datasets for groundwater have great value, as they do for all components of environmental monitoring. However, there is a global scarcity of such datasets, with few extending beyond 30 years and even fewer to 50 or 100 years. Hydrogeological investigations and groundwater management activities benefiting from long-term data are listed, the successful use of such datasets illustrated, and the reasons for their scarcity discussed.

Abstract We identify for the first time an Indian Easterly Jet (IEJ) in the mid‐troposphere during the pre‐monsoon using reanalysis data. The IEJ is weaker and smaller than the African Easterly Jet over West Africa, with a climatological location of 10°N, 60–90°E, 700 hPa, and strength 6–7 m s−1 during March–May. The IEJ is a thermal wind associated with low‐level meridional gradients in temperature (positive) and moisture (negative), resulting from equatorward moist convection and poleward dry convection. The IEJ is associated with a negative meridional potential vorticity gradient, therefore satisfying the Charney‐Stern necessary condition for instability. However, no wave activity is detected, suggesting that the potential for combined barotropic‐baroclinic instability is not often realized. IEJ strong (weak) years feature increased (reduced) near‐surface temperatures and drier (wetter) conditions over India. This study provides an introduction to the IEJ's role in pre‐monsoon dynamics, and a platform for further research.

Introduction: Previous observational studies have suggested an association between endometriosis and higher risk of cardiovascular disease. However, in the observational setting residual confounding could drive the association. Hypothesis: In this study, we hypothesize that endometriosis might be causally related to cardiac disease, structure and function, and investigate this using Mendelian randomization (MR), a method that is not liable to influence by observational confounding. Methods: Genome-wide significant (p<5x10-8) uncorrelated (r2<0.001) variants associated with endometriosis in a recent genome-wide association study of 60,674 cases and 701,926 controls were extracted as instrumental variants. Gene-outcome association data was extracted from GWAS of atrial fibrillation, coronary artery disease, heart failure, ischaemic stroke, and 15 different indexed cardiac magnetic resonance markers of structure and function. Inverse-variance weighted MR was used for primary analysis. A further exploratory analysis was performed for the outcome of atrial fibrillation, extracting data from a different GWAS study, to further explore to a borderline result on primary analysis. Results: A higher genetically-predicted risk of endometriosis was not associated with risk of atrial fibrillation (OR 1.06, 95%CI 1.00 to 1.12, p=0.059), coronary artery disease (OR 1.00, 95%CI 0.93 to 1.07, p=0.933), heart failure (OR 1.00, 95%CI 0.94 to 1.05, p=0.920), or ischaemic stroke (OR 0.97, 95%CI 0.91 to 1.03, p=0.342). Replication analysis using a different data source for atrial fibrillation corroborated a lack of association (OR 1.02, 95%CI 0.96 to 1.09, p=0.488). There were no associations with any of measures of cardiac structure and function, as displayed in Figure 1. Conclusions: The results do not support a causal role of endometriosis in determining the risk of cardiovascular diseases, and in promoting an adverse cardiac structure and function.