Drained peatlands are major sources of greenhouse gas (GHG) emissions. Reducing these is essential for achieving climate mitigation targets and agricultural net-zero goals. Water table depth (WTD) strongly influences emissions, yet national assessments often overlook the role of drain water levels within complex lowland drainage networks in controlling WTD. The objective of this study was to test a drainage-constrained field-scale modelling framework for estimating how raised ditch-water levels alter water-table dynamics and modelled CO₂-CH₄ emissions across managed lowland peat fields. LiDAR-derived hydrological data were integrated with a physically based daily water table model to generate a spatially explicit assessment of field-scale carbon dioxide (CO₂) and methane (CH₄) emissions from drained lowland peat soils in Eastern England. Emissions were estimated under baseline drainage conditions and a set of rewetting scenarios that progressively raised drain water levels while maintaining hydrological connectivity. Results indicate that raising drain water levels reduced modelled landscape GHG emissions by up to 58% relative to baseline. Most mitigation benefits were achieved in the initial stages of rewetting, with successive scenarios delivering progressively smaller gains (mean modelled GHG emissions decreases by ∼5.3 tCO₂eq/ha/yr between baseline and scenario 3, but by only ∼0.01 tCO₂eq/ha/yr between scenario 10 and scenario 13, indicating a near-plateau with negligible additional mitigation). Beyond moderate increases in water level, further reductions in CO₂ emissions were offset by rising CH₄ emissions, highlighting a clear trade-off between reduced CO₂ losses and enhanced CH₄ release. By explicitly resolving fine-scale hydrological variability, we demonstrate the importance of spatially detailed modelling for capturing the variability in the drivers of modelled peatland GHG emissions and guiding mitigation strategies. Realising the full benefits of rewetting will require sustained summer water supplies, modifications to drainage infrastructure to improve field-level water management, and economic mechanisms that reward rewetting.

Carbon dioxide removal (CDR) is an essential measure for achieving national and international climate targets, complementing emission reductions. Germany aims for net-zero greenhouse gas emissions by 2045 and net-negative emissions thereafter. Nonetheless, current deployment levels are still a far cry from what is needed. This study employs a comprehensive modeling approach to examine the regional deployment of bio-based CDR in Germany, integrating techno-economic and regional conditions. We focus on two key CDR approaches: technical and natural sinks, thereby coupling energy and non-energy sectors and providing a cross-sectoral view. The selected CDR concepts are implemented in the extended bioenergy optimization model (BENOPTex) to assess their deployment for achieving climate targets cost-effectively in Germany. Our analysis demonstrates that a co-deployment strategy of technical and natural sinks, as opposed to deploying solely technical sinks, can enhance overall CDR potential by at least 50% while cutting removal costs, achieving around ∼110 Mt CO2 removal annually by 2050.

Fatigue is a critical factor contributing to operational errors, traffic accidents, and psychological disorders. A major challenge in current fatigue detection lies in the difficulty of clearly identifying fatigue-related neural channels in the brain, which hinders the accurate extraction of effective features from high-dimensional electroencephalography (EEG) signals.

Magnetic skyrmions are vortex-like spin textures that exhibit remarkable emergent phenomena and hold great promise for applications in spintronics, magnonic devices, and neuromorphic computing. Typically, these spin textures are formed in magnetic samples through the application of magnetic fields on a ground state helical phase. However, achieving the formation of zero-field skyrmions, without the need of prior field-cooling, has remained a significant challenge. Here, using real-space Lorentz transmission electron microscopy, we directly observe the spontaneous emergence of skyrmions beyond room temperature in a centrosymmetric crystal of boron-doped CrTe without the presence of external magnetic fields. These skyrmions exhibit robust stability over a large temperature range in zero field and various specimen thicknesses. We nevertheless demonstrate that a field-cooling process can control the density and arrangement of metastable zero-field skyrmions, which is confirmed by micromagnetic simulations. The stabilization of field-free spontaneous skyrmions beyond ambient temperature holds implications for nonvolatile memory systems.

Accurate and timely identification of brain tumours from Magnetic Resonance Imaging (MRI) scans remains one of the most clinically consequential problems in medical image analysis, because the four principal tumour categories - glioma, meningioma, pituitary adenoma, and the tumour-free condition - share overlapping intensity profiles and highly variable morphological presentations. This research introduces BrainFusionNet, a novel deep learning pipeline built on an ImageNet-pre-trained EfficientNet-B4 backbone augmented with a Convolutional Block Attention Module (CBAM) and trained with a hybrid loss combining label-smoothing cross-entropy and focal loss. A structured two-phase fine-tuning strategy - frozen early layers during warm-up, followed by full-network unfreezing under a Cosine Annealing with Warm Restarts (CAWR) schedule - maximises generalisation from a moderately sized dataset. At inference time, a five-fold Test-Time Augmentation (TTA) ensemble further sharpens predictions. Evaluated on the publicly available Kaggle Brain Tumour MRI dataset comprising 7-200 annotated scans across four classes, BrainFusionNet achieves a test accuracy of 99.81%, a macro-averaged F1-score of 99.78%, and a mean AUC of 0.9994 - surpassing every compared baseline, including ResNet-50, DenseNet-201, EfficientNet-B4 (standalone), and ViT-B/16, with statistical superiority confirmed by McNemar tests (p< 0.0001 for all comparisons). Cross-institutional validation on the BraTS 2020 dataset (19 institutions, three scanner vendors, zero-shot transfer) yields 98.25% accuracy and AUC = 0.9962, demonstrating robust generalisation to unseen multi-scanner clinical data. Grad-CAM visualisations confirm that the model attends to diagnostically meaningful anatomical regions, and t-SNE embeddings reveal clearly separable inter-class clusters in the learned feature space.

Nutrient sensing enables the body to detect and respond to nutrient availability through hormonal regulation. In this study, we examined nutrient-stimulated hormonal responses in children with severe acute malnutrition (SAM) compared with hospitalized (HC) and community (CC) child comparators. Basal and fed blood samples were collected from participants before and after consumption of a liquid meal. In the basal state, children with SAM showed significantly higher circulating concentrations of insulin, C-peptide, peptide tyrosine tyrosine (PYY), ghrelin, glucagon, glucagon-like peptide 1 (GLP-1) and glucagon like peptide 2 (GLP-2), in comparison to one or both comparators. After feeding, insulin, C-peptide, cholecystokinin, PYY and GLP-1 increased in all groups, indicating some level of preserved nutrient sensing. GLP-2 increased significantly in SAM and hospitalized children, and a reduction in ghrelin was observed in children with SAM. No significant changes were detected for glucagon, leptin or secretin in any group. Disordered responses, defined as zero or positive postprandial responses in ghrelin and zero or negative postprandial responses in the other hormones, were seen in all children. Significantly higher percentages of children with SAM had disordered responses compared with the non-malnourished children, particularly for insulin (23%) and C-peptide (16%), suggestive of altered pancreatic hormonal regulation in SAM. These findings highlight the need for improved nutritional interventions for affected children.

Single-photon purity is one of the most important key metrics of many quantum states of light. For applications in photonic quantum technologies, e.g. quantum communication and linear optical quantum computing, a minimization of the multi-photon error rate is required because of its error-introducing nature. Ultimately, the purity of state-of-the-art single-photon sources was found to be limited by spontaneous emission and subsequent reexcitation during the interaction with the driving field. Here, we demonstrate that even this fundamental limit to the single-photon purity can be overcome due to the distinct spectro-temporal properties of the individual photons forming multi-photon errors. For driving pulses shorter than the emitter lifetime, we find that photons emitted during the pulse exhibit a significantly broader spectral shape than the emitter's natural linewidth. Thus, we can selectively filter out the majority of this instantaneously emitted photon by employing narrowband spectral filters, which reduces the measured degree of second-order coherence at zero time delay by almost one order of magnitude. This enables a significant suppression of the multi-photon error rate without detrimental effects on the desired single-photon emission.