Communication and public education are vital to increasing community resilience to natural hazards. Recent disaster risk education initiatives have challenged top-down hierarchical models of outreach by embracing community-led, participatory and co-creation approaches. Here, we evaluate the process of developing the Disaster Ready! boardgame through community co-creation and co-design methods with students and local educational stakeholders. Discussions with communities directly informed the content and play style of the game, and input from student participants shaped the artistic evolution of the game board to include important cultural, social and environmental symbols. The final version of the game is therefore more locally representative and rooted in the lived experiences of communities in rural Nepal. The process of co-creation also facilitated the collaborative development of knowledge on natural hazards through participant-led discussions and dialogues. We highlight that stakeholder and community participation throughout the development phase of serious games, and in other disaster risk reduction (DRR) resources, is vital to producing bespoke and contextually relevant resources for DRR education. Central to the longer-term success and continued use of the game in Nepali schools was also the transfer of agency over the game to local educational facilitators. Through reflexive discussion on our co-creation approach we highlight the limitations in our method and the challenges in creating a fully co-produced resource. This includes the influence of power dynamics and hierarchies of perceived knowledge, where an imbalance in authority over the co-creation process may limit the creative input of the community. We suggest that overcoming these challenges to achieve higher levels of community participation in the co-production process will require greater planning on the part of researchers, as well as funding pathways that support longer term development of trusting, reciprocal and creative relationships with communities and local stakeholders.
In quantum field theory, the algebraic existence of a field does not guarantee the existence of a corresponding localized asymptotic particle state. This distinction is well established in the presence of infrared effects, long-range correlations, and environmental interactions, and becomes particularly relevant in supersymmetric theories, where fermionic and bosonic degrees of freedom are constrained at the algebraic level but need not share identical asymptotic behavior. In this work we introduce a minimal and predynamical localization criterion that distinguishes algebraically allowed degrees of freedom from those capable of forming stable, phasecoherent asymptotic states. The criterion is formulated in terms of long-time stability under slow structural fluctuations of an effective background, without modifying the underlying field equations or introducing new physical interactions. We show that fermionic and scalar fields respond qualitatively differently to such structural effects. While fermionic modes may retain asymptotic stability, scalar modes generically exhibit decoherence and damping, preventing their interpretation as localized one-particle states. This provides a conservative and model-independent perspective on how supersymmetric algebraic structures may coexist with an asymmetric observable particle spectrum. The analysis is intentionally non-constructive and does not rely on specific supersymmetrybreaking mechanisms, cosmological assumptions, or new dynamical ingredients. Rather, it clarifies localization as an independent structural requirement for particle existence within standard quantum field theory.
<p>Earth system tipping points pose existential threats to current and future generations, both human and non-human, with those least responsible for causing them facing the greatest risks. “Positive” social tipping points (that we shorten to positive tipping points, or PTPs) are often deliberate interventions into social systems with the aim of rapidly mitigating the risks of Earth system tipping. However, the desire to intervene should neither increase risks nor perpetuate unjust or inequitable outcomes through the creation of sacrifice zones. In this paper, we argue that considerations of what needs to change, who is being asked to change, and where and by whom the impacts of change will be felt are fundamental and normative questions that require reflexivity and systemic understanding of decision-making across scales. All actors have a role to play in ensuring that justice, equity, and ethics are carefully considered before any intervention. Enabling positive tipping points for radical transformations would thus benefit from more diverse perspectives, with a particular emphasis on the inclusion of marginalized voices in offering solutions. We conclude that taking a cautious approach to positive tipping interventions, including careful consideration of distributional and unintended consequences, and stepping back to explore all options, not just those appearing to offer a quick fix, could lead to more equitable and sustainable outcomes.</p>
This study approaches the characterization of Ferroxite and Hematite and the test of their magnetic properties on the degradation and adsorption of Atrazine, an herbicide of the triazine class. This herbicide was compared with a sample of Ferroxite in the absence of a magnetic field and with Hematite, a non-magnetic material which should not be attracted by the magnet. In the sample, the Atrazine determination was carried out by Fenton analysis. Preliminary results were satisfactory, gathering a reduction rate up to 85% for Ferroxite in the presence of a magnetic field and 53% for Hematite. The Fenton reaction, however, showed an 87% reduction rate for Ferroxite in the presence of a magnetic field, and 56% for Hematite. These findings have shown that there is a relation between the magnetic field intensity and the adsorption capacity for these materials.
<p>Planetary-scale forestation has been shown to induce global surface warming associated with a slowdown of the Atlantic Meridional Overturning Circulation (AMOC). This AMOC slowdown is accompanied by a negative North Atlantic sea surface temperature (SST) anomaly resembling the known North Atlantic warming hole found in greenhouse gas forcing simulations. Likewise, a reversed equivalent of the SST response has been found across deforestation experiments. Here, we test the hypothesis that localised forest cover changes over North America are an important driver of this response in the downstream North Atlantic Ocean. Moreover, we shine a light on the physical processes linking forest cover perturbations to ocean circulation changes. To this end, we perform simulations using the fully coupled Earth system model CESM2, where pre-industrial vegetation-sustaining areas over North America are either completely forested (“forestNA”) or turned into grasslands (“grassNA”). Our results show that North American forest cover changes have the potential to alter the AMOC and North Atlantic SSTs in a manner similar to global ones. North American forest cover changes mainly impact the ocean circulation through modulating land surface albedo and, subsequently, air temperatures. We find that comparably short-lived cold-air outbreaks (CAOs) play a crucial role in transferring the signal from the land to the ocean. Around 80 % of the ocean heat loss in the Labrador Sea occurs within CAOs during which the atmosphere is colder than the underlying ocean. A warmer atmosphere in forestNA compared to the “control” scenario results in fewer CAOs over the ocean and thereby reduced ocean heat loss and deep convection, with the opposite being true for grassNA. The induced SST responses further decrease CAO frequency in forestNA and increase it in grassNA. Lagrangian backward trajectories starting from CAOs over the Labrador Sea confirm that their source regions include (de-)forested areas. Furthermore, the subpolar gyre circulation is found to be more sensitive to ocean density changes driven by heat fluxes than to changes in wind forcing modulated by upstream land surface roughness. In forestNA, sea ice growth and the corresponding further reduction in ocean-to-atmosphere heat fluxes forms an additional positive feedback loop. Conversely, a buoyancy flux decomposition shows that freshwater forcing only plays a minor role in the ocean density response in both scenarios. Overall, this study shows that the North Atlantic Ocean circulation is particularly sensitive to upstream forest cover changes and that there is a self-enhancing feedback between CAO frequencies, deep convection, and SSTs in the North Atlantic. This motivates studying the relative importance of these high-frequency atmospheric events for ocean circulation changes in the context of anthropogenic climate change.</p>
AlphaFold 3 represents a transformative advancement in computational biology, enhancing protein structure prediction through novel multi-scale transformer architectures, biologically informed cross-attention mechanisms, and geometry-aware optimization strategies. These innovations dramatically improve predictive accuracy and generalization across diverse protein families, surpassing previous methods. Crucially, AlphaFold 3 embodies a paradigm shift toward differentiable simulation, bridging traditional static structural modeling with dynamic molecular simulations. By reframing protein folding predictions as a differentiable process, AlphaFold 3 serves as a foundational framework for integrating deep learning with physics-based molecular
Explicit structural information has been proven to be encoded by Graph Neural Networks (GNNs), serving as auxiliary knowledge to enhance model capabilities and improve performance in downstream NLP tasks. However, recent studies indicate that GNNs fail to fully utilize structural information, whereas Multi-Layer Perceptrons (MLPs), despite lacking the message-passing mechanisms inherent to GNNs, exhibit a surprising ability in structure-aware tasks. Motivated by these findings, this paper introduces a comprehensive probing framework from an information-theoretic perspective. The framework is designed to systematically assess the role of explicit structural modeling in enhancing language model (LM) representations and to investigate the potential of MLPs as efficient and scalable alternatives to GNNs. We extend traditional probing classifiers by incorporating a control module that allows for selective use of either the full GNN model or its decoupled components, specifically, the message-passing and feature-transformation operations.This modular approach isolates and assesses the individual contributions of these operations, avoiding confounding effects from the complete GNN architecture. Using the Edge Probing Suite, a diagnostic tool for evaluating the linguistic knowledge encoded in LMs, we find that MLPs, when used as feature-transformation modules, consistently improve the linguistic knowledge captured in LM representations across different architectures. They effectively encode both syntactic and semantic patterns. Similarly, GNNs that incorporate feature-transformation operations show beneficial effects. In contrast, models that rely solely on message-passing operations tend to underperform, often leading to negative impacts on probing task performance.
To reduce the cost and consumption of computing resources caused by computational redundancy and delayed reward assignment in long CoT, this research proposes the dynamic chain-of-thought (D-CoT) with adaptive reasoning time and steps. The researcher used simulation experiment to simulate the integration of D-CoT through Python 3.13 IDLE combined with a Python simulator based on GPTs. At the same time, the researcher used DeepSeek R1 as a control group to test and compare the performance of the D-CoT simulator in processing MIT OpenCourseWare's linear algebra exam questions. Experimental results show that D-CoT is better than DeepSeek R1 based on long CoT in three indicators: reasoning time, CoT length (reasoning steps) and token count, which achieves a significant reduction in computing resource consumption. In addition, this research has potential value in deep reasoning optimization that is used as a reference for future dynamic deep reasoning frameworks.
Cataldo Godano, Anna Tramelli, Giuseppe Petrillo
et al.
A very efficient method for estimating the completeness magnitude mc and the scaling parameter b of earthquake magnitude distribution has been thoroughly tested using synthetic seismic catalogues. Subsequently, the method was employed to assess the capability of the b-value in differentiating between foreshocks and aftershocks, confirming previous findings regarding the Amatrice-Norcia earthquake sequence. However, a blind algorithm reveals that the discriminative ability of the b-value necessitates a meticulous selection of the catalogue, thereby reducing the predictability of large events occurring subsequent to a prior major earthquake.
Benjamin Fernando, Constantinos Charalambous, Christelle Saliby
et al.
Hypersonic re-entries of spacecraft are valuable analogues for the identification and tracking of natural meteoroids re-entering the Earth's atmosphere. We report on the detection of seismic and acoustic signals from the OSIRIS-REx landing sequence, acquired near the point of peak capsule heating and recorded using a fully off-grid Raspberry PiShake sensor. This simple setup is able to record all the salient features of both the seismic and acoustic wavefields; including the primary shockwave, later reverberations, and possible locally induced surface waves. Peak overpressures of 0.7 Pa and ground velocities of 2x10-6m/s yield lower bound on the air-to-ground coupling factor between 3 and 44 Hz of 1.4x10-6 m/s/Pa, comparable to results from other re-entries
Finn Schmidt, Polina Turishcheva, Suhas Shrinivasan
et al.
The neural activity in the visual processing is influenced by both external stimuli and internal brain states. Ideally, a neural predictive model should account for both of them. Currently, there are no dynamic encoding models that explicitly model a latent state and the entire neuronal response distribution. We address this gap by proposing a probabilistic model that predicts the joint distribution of the neuronal responses from video stimuli and stimulus-independent latent factors. After training and testing our model on mouse V1 neuronal responses, we find that it outperforms video-only models in terms of log-likelihood and achieves improvements in likelihood and correlation when conditioned on responses from other neurons. Furthermore, we find that the learned latent factors strongly correlate with mouse behavior and that they exhibit patterns related to the neurons' position on the visual cortex, although the model was trained without behavior and cortical coordinates. Our findings demonstrate that unsupervised learning of latent factors from population responses can reveal biologically meaningful structure that bridges sensory processing and behavior, without requiring explicit behavioral annotations during training.
This study proposes a methodology to utilize machine learning (ML) for topology optimization of periodic lattice structures. In particular, we investigate data representation of lattice structures used as input data for ML models to improve the performance of the models, focusing on the filtering process and feature selection. We use the filtering technique to explicitly consider the connectivity of lattice members and perform feature selection to reduce the input data size. In addition, we propose a convolution approach to apply pre-trained models for small structures to structures of larger sizes. The computational cost for obtaining optimal topologies by a heuristic method is reduced by incorporating the prediction of the trained ML model into the optimization process. In the numerical examples, a response prediction model is constructed for a lattice structure of 4x4 units, and topology optimization of 4x4-unit and 8x8-unit structures is performed by simulated annealing assisted by the trained ML model. The example demonstrates that ML models perform higher accuracy by using the filtered data as input than by solely using the data representing the existence of each member. It is also demonstrated that a small-scale prediction model can be constructed with sufficient accuracy by feature selection. Additionally, the proposed method can find the optimal structure in less computation time than the pure simulated annealing.
Anastasia V. Artemova, Sergey S. Maklakov, Artem O. Shiryaev
et al.
The relationship between the chemical purity of one-size particles and microwave properties in ferromagnetic materials is not clearly studied. Ferromagnetic nanostructured iron powders were synthesized from iron nitrate solution using ultrasonic spray-pyrolysis and then reduced in H<sub>2</sub> flow at 350, 400, 450, and 500 °C. A rise in the concentration of solutions of a precursor from 10 to 20 wt. % led to an increase in mean particle size. The interrelationship was studied between chemical composition and the microwave dispersion of the powders obtained. An increase in the temperature of reduction changes the chemical composition and increases the amplitude of complex microwave permeability, which was studied using solid-state physics methods (XRD, STA, SEM, and VNA). It was found that annealing at 400 °C is the optimal treatment that allows the production of iron powders, consisting of about 90% of α-Fe phase, possessing a particle surface with low roughness and porosity, and demonstrating intense microwave absorption. Annealing at a higher temperature (500 °C) causes an even higher increase in permeability but leads to the destruction of nanostructured spheres into smaller particles due to grain growth. This destruction causes an abrupt increase in permittivity and therefore significantly reduces potential applications of the product. The insight into chemical–magnetic relationships of these materials enhances the data for design applications in magnetic field sensing.