Climate change amplifies many threats to human health. Despite advances in understanding climate change dynamics and impacts, there remains a critical gap in translating scientific knowledge into equitable, and community-driven health interventions. The inaugural One Earth, One Health workshop sought to explore this gap through human-centered design exercises involving interdisciplinary researchers from climate and Earth sciences, engineering, epidemiology, microbiology, and environmental health. Although participants did not co-develop solutions with affected communities, they used stakeholder role-playing to guide ideation and lay groundwork for actionable plans. Through these methods, participants identified community needs and proposed prototype solutions to alleviate health threats exacerbated by global environmental change. Prototypes were organized around infectious diseases, extreme weather, and air quality, as illustrative themes rather than an exhaustive set of risks. Key solutions included strategies for anticipatory systems and early warning (e.g., integrating environmental signals with health data), inclusive communication and infrastructure needs for responding to extreme weather events, and integrated platforms visualizing air quality trends to support tailored, context-aware guidance beyond one-size-fits-all alerts. The workshop highlighted opportunities such as leveraging machine learning, Earth observation, and real-time surveillance to protect communities, but also noted barriers including data quality, technological redundancy, privacy, and governance challenges. Additionally, participants emphasized the need for interdisciplinary teams capable of collaborating across sectors, breaking down silos and addressing gaps in training and education. Overall, the workshop illustrates how process-driven, human-centered approaches can help surface user needs and generate testable prototype concepts, while underscoring the importance of direct community partnership for implementation. Climate and other environmental changes amplify threats to human health, such as extreme weather events, infectious diseases, and poor air quality. When trying to understand which hazards and exposures pose the risk to human health, which populations are most vulnerable, and what interventions might be most protective, scientists rely on hypothesis‐driven approaches. Such approaches may not directly reflect the lived experiences or priorities of affected communities. The One Earth, One Health workshop congregated researchers across disciplines to test a method called human‐centered design. Although this workshop did not include direct participation from every key stakeholder groups, participants role‐played as community members, such as healthcare workers, city planners, parents, and concerned citizens, to simulate more inclusive solution development. Participants discussed and co‐developed early‐stage, user‐centered solutions, such as better disease prediction tools, clearer emergency communications, and unified platforms for air quality monitoring and alerts. Although promising, the solutions face multiple challenges, including limitations in data availability, timeliness, and interoperability and technological complexity. The workshop underscored the importance of collaboration and co‐creation as guiding principles for future climate‐health research and intervention design, including the need to engage researchers, policymakers, healthcare workers, and communities in subsequent phases to support practical, equitable, and beneficial outcomes.
The accumulation of free oxygen in the atmosphere ≈2.45 billion years ago was one of the most transformative events in Earth's past. Since its identification, this interval termed the 'Great Oxidation Event' (GOE), has garnered a large amount of attention from a wide array of perspectives, with some suggesting it should define its own geologic era or period. Despite many new tools to interrogate the GOE, today the defendable range of possible atmospheric O2 levels span orders of magnitude, begging the question, how great was Earth's Great Oxidation? The consequences of such disparate views on oxygenation levels are uncertainties regarding biospheric evolution, interpretations of the sedimentary record, and a limited ability to translate Earth's atmospheric history into insights for exoplanet research. In this review, we revisit the conditions immediately before, during, and after Earth's GOE to explore the key assumptions that underlie differing views on this critical interval of time. We then highlight new discoveries and outline extremely divergent but defendable interpretations of atmospheric oxygen trajectories across the GOE. Reducing such divergent scenarios should be a major target of research progress in the coming years.
Urban environmental monitoring networks frequently encounter significant data gaps due to sensor malfunctions, environmental disturbances, and communication failures. Reliable approaches to address these gaps are essential for ensuring the continuity and quality of environmental data streams. In this study, we developed a gated attention bidirectional long short-term memory (GA-BiLSTM) model to impute missing data in a dense urban monitoring network. Using observations from the CROCUS network in Chicago, we evaluated GA-BiLSTM against widely used approaches (XGBoost and K-nearest neighbors) under scenarios of both short-term intermittent gaps and prolonged outages. GA-BiLSTM consistently outperformed comparative methods, particularly during extended outages of up to ten days, demonstrating its ability to capture spatiotemporal dependencies across sensor nodes. Beyond performance metrics, feature importance and spatial network analyses highlighted the unexpected but critical predictive role of peripheral rural nodes, underlining their strategic value for maintaining robust urban monitoring systems. These results emphasize that advanced imputation methods can substantially improve the reliability of environmental monitoring networks and support more resilient data infrastructures for urban sustainability.
Long-duration human spaceflight will require medical systems capable of managing illness and injury without rapid evacuation or real-time assistance from Earth. Microgravity physiology, engineering limits, and communication delays reduce the feasibility of conventional surgery and favor imaging-based, minimally invasive approaches. Expeditionary interventional radiology can be defined as a practice model emphasizing image-guided, minimally invasive procedures delivered with compact equipment by small, cross-trained teams in resource-constrained environments. Research shows that astronauts and other non-specialists can obtain diagnostic-quality ultrasound images in microgravity, and analog studies demonstrate that individuals with little experience can learn key ultrasound-guided tasks after focused instruction. These findings support the feasibility of image-guided drainage, decompression, and vascular access as candidate strategies for managing acute conditions encountered during exploration missions. Remaining challenges include procedural ergonomics, equipment design, sterility, fluid containment, and development of autonomous guidance tools. This narrative review outlines a streamlined approach for adapting interventional radiology to spaceflight and highlights research needs for achieving procedural autonomy beyond Earth.
Heat stress limits for human survivability have been previously defined by a 6-hour exposure to a wet-bulb temperature of 35oC. However, the recently developed physiology-based HEAT-Lim model demonstrates that environmental heat stress thresholds may be cooler and drier than previously thought. We employ HEAT-Lim to determine whether non-survivable thresholds were surpassed during six historical events where conditions were climatologically extreme and/or high heat-related mortality was reported. Our results show that non-survivable conditions are occurring during present-day heat events, all of which are below 35oC wet-bulb temperature. Of concern is regular exceedances of deadly thresholds for older people directly exposed across all events. Moreover, extremely hot yet dry conditions are found to be just as deadly as hot and humid conditions. For future climatological assessments, we emphasise the importance of employing increasingly accurate physiology-derived methods to assess the risk of potentially deadly heat stress.
What socioecological conditions nurture the ingenuity and collaborative interactions underlying transformative technological innovations? We compiled a dataset on more than 400 major technological inventions from 1690 to 1990 spanning seven categories (agriculture, armaments, information and communication, household, industry, medical, and transportation) and performed inductive macroecological analyses to address how attributes of inventors, teams, and their social and geographic environments contributed to the innovation of new technologies that have changed the way people live. The vast majority of inventions were attributed to single inventors of various ages. The frequency, size, and cultural diversity of teams increased in the 20th Century, as did the proportion of inventions attributed to women and immigrants. A wide variety of environments acted as innovation hubs, including rural areas as well as specialized institutions and large cities. Invention rate (number of inventions per time period) peaked during the 1800s in cities, rural areas, and most categories but continued increasing over time in institutional environments and medical and communication technologies. The overall pattern across ages, genders, team attributes, environments, technological categories, and time periods supports the conclusion that opportunity, ingenuity, and environment all play key roles in the inventiveness phase of the innovation process: creative individuals in particular ecological environments and social settings come up with novel solutions to specific practical problems. Similar innovation patterns have been observed for tool use among nonhuman primates, highlighting unifying processes of innovation. Our macroecological approach offers valuable insights into the emergence and drivers of innovation and material culture.
This paper proposes a turbulence-induced perturbation (TIP) approach to address the security degradation of quantum noise stream cipher (QNSC) systems under high transmit power and introduces a variable-order quantum noise stream cipher (VO-QNSC) scheme to further enhance transmission performance. The TIP approach incorporates turbulence-induced perturbation to strengthen the physical-layer security of QNSC in high-power scenarios, while the VO-QNSC scheme significantly improves system performance without increasing algorithmic complexity or redundancy, making it suitable for deployment on satellite terminals with limited computational resources. Simulation results show that, after introducing TIP, the system detection failure probability (DFP) can exceed 99.97% and number of masked signals (NMS) is improved to the order of 103, which effectively enhances the anti-eavesdropping ability of the system. For poor channel conditions, VO-QNSC can improve the receiver sensitivity by up to approximately 0.5 dB, which can meet the requirements of communication security and transmission performance in complex environments.
Greenhouse gas (GHG) emissions are crucial for monitoring and mitigating climate change and the degradation of sensitive biomes. Such demand motivates the search for automated processes to collect and manage GHG emissions data, with open access to researchers and institutions working with sustainability. This work proposes an automated data collection process using low-cost drones, with direct data transfer to a cloud-based data space. Low-cost drones were equipped with onboard sensors to measure CO 2 and methane emissions. The focus was not on data accuracy but on automating data collection and transmission, drone design specifications, and testing, exploring the balance between data accuracy and low-cost sensors. The first practical proof-of-concept experiments demonstrating the system's capabilities used a drone prototype with simple sensors in an outdoor campus environment, sending data to a cloud-based data space called Digital Amazon (intended to store GHG emissions from the Amazon Forest), via 4G internet communication network. The system's design addressed aspects such as avoiding interference during data collection and trajectory adjustment, data transfer, and finalizing dataset composition in the cloud. The results provide initial evidence supporting the feasibility of the proposed system in an outdoor environment. However, its application to more complex scenarios, such as forests, other biomes, or urban areas, will be explored in subsequent research based on the reference model presented and will require further validation under diverse environmental and operational conditions. Enhancements to accommodate future communication based on Low Earth Orbit (LEO) and Very Low Earth Orbit (VLEO) satellite systems would help reduce transmission latency, but this issue was not assessed in the present study.
Marine bacteria are present almost everywhere in the ocean environment and are essential to many biogeochemical processes. The perspectives of ecologists and evolutionary biologists on the significance of microbes in ecosystem function are shifting as a result of exploring the marine microbiomes. This is especially true in ocean habitats, where microbes comprise the bulk of the biomass and are responsible for the majority of the planet's key biogeochemical cycles, including those that influence the global climate. Emerging research suggests that many ecosystem services provided by coastal marine environments depend on intricate interactions between groups of microbes and the environment or their hosts. The structure, variety, and functional capability of marine microbial populations have been revealed on a global scale thanks to recent developments in molecular ecology techniques. Over-recent-decades, industrialization and urbanization have led to widespread contamination of oceans. These contaminants accumulate in seawater and sediments, particularly in coastal areas, posing risks to marine ecosystems and human health. Marine microorganisms possess diverse catalytic abilities and extreme environmental tolerance, making them suitable for bioremediation of toxins. Effective-degradation of pollutants often depends on syntrophic-interactions within microbial communities, highlighting the importance of understanding their collaboration and communication for marine resource management. Here, we assess the current level of knowledge about marine microbiome research and highlight key issues within this developing field of study. The review aims to enhance understanding of marine microbiome's roles and potential uses in biogeochemical analysis, biotechnology, and environmental remediation, which could support sustainable and circular business models for future generations.
Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage expose semantic transmissions to severe eavesdropping risks. This paper establishes a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks. In particular, we first develop an integrated mathematical model that couples the semantic representation process, physical-layer satellite propagation characteristics, and information-theoretic secrecy into a single analytical formulation. By defining a joint semantic security cost function, the antagonistic trade-off between semantic fidelity and secrecy capacity is quantitatively characterized under realistic power, beamforming, and propagation constraints. To balance semantic fidelity and information secrecy, a reinforcement-learning-based optimization framework is proposed, wherein an actor-critic agent learns optimal power allocation and semantic weighting strategies through continuous interaction with the environment. This learning-based optimization approach enables autonomous control without requiring explicit channel distribution knowledge or offline parameter tuning. Extended simulation results show that the proposed approach consistently enhances both semantic fidelity and secrecy performance compared with conventional power-control schemes and demonstrate its potential as a foundational architecture for secure and intelligent semantic communications in next-generation satellite networks.
Plant volatile organic compounds (VOCs) represent one of the most dynamic and integrative biochemical signaling systems linking molecular plant stress responses to ecosystem-level processes. This review provides an integrative cross-scale framework for understanding the biochemical pathways, regulatory networks, ecological functions, and technological applications of stress-induced volatile emissions. At the molecular and cellular levels, VOC emissions are regulated through complex enzymatic and hormonal pathways involving jasmonates, salicylates, ethylene, and abscisic acid, enabling plants to respond rapidly to abiotic and biotic stressors such as drought, herbivory, temperature extremes, salinity, and atmospheric pollution. These volatile signals extend beyond individual plants, functioning as mediators of plant-plant communication, plant-microbe interactions, and multi-trophic ecological networks that shape community dynamics and ecosystem resilience. Recent technological advancements, including mass spectrometry platforms, remote sensing systems, biosensors, and artificial intelligence-driven analytical frameworks, have transformed the ability to detect, interpret, and predict stress-induced VOC emissions in real time. Integrating these technologies with multi-omics datasets and digital twin modeling enables the development of predictive monitoring systems capable of scaling plant stress detection from agricultural fields to regional ecosystems. Despite these advances, significant challenges remain, including variability in emission profiles across species and environments, atmospheric transformation of volatile signals, methodological inconsistencies, and limitations in large-scale monitoring infrastructure. Future research should focus on establishing global networks for monitoring plant volatiles, standardized measurement protocols, and integrated biosensing infrastructures that can link plant stress signals to Earth-system observations. Decoding plant volatile stress signaling across scales offers a transformative pathway to advance climate-resilient agriculture, biodiversity conservation, and predictive environmental intelligence systems that support adaptive ecosystem management in an era of accelerating environmental change.
The National Aeronautics and Space Administration (NASA) is planning exploration space missions to Mars, which will require plans for a wide array of medical contingencies, most of which require treatment with medications. A major challenge is that medications cannot be resupplied beyond Earth orbit, and many medications will exceed their labeled expiration date over the duration of an exploration-class mission. Furthermore, the spaceflight environment may alter the rate or pathways involved in the degradation of active pharmaceutical ingredients (APIs). However, the stability of only a handful of drugs have ever been tested after prolonged exposure to spaceflight. Existing ground-based drug stability studies do not include key factors associated with spaceflight, such as levels of carbon dioxide (CO2) and ionizing radiation that are significantly higher than on Earth. Therefore, there is a risk that some expired or degraded medications will accumulate hazardous impurities, that, at sufficiently high doses, could cause acute or long-term health effects in astronauts. To address this risk, an assessment framework is proposed based on the accepted principles of chemical risk assessment. This communication describes the five steps of the proposed risk assessment framework, which are: (1) defining the use scenario of the drug, (2) identifying the API degradants, (3) assessing hazards and dose-response of the degradation products, (4) assessing the dose of degradant products, and (5) characterizing the risk for adverse health effects. To predict and identify drug degradants, this framework leverages known chemical reaction pathways and API chemistry (i.e. susceptible moieties) and data from stability studies. The framework focuses on health effects of greatest concern: high acute toxicity, sensitization, and mutagenicity and carcinogenesis. Hazard analysis uses chemical hazard databases, in silico prediction tools, and available terrestrial and spaceflight drug degradation studies. Risk is characterized relative to established health-based exposure limits (HBELs) or threshold of toxicological concern values (TTCs). A companion article describes case studies that apply this framework to four different classes of APIs under consideration for use during exploration spaceflight: azithromycin, diclofenac, gabapentin, and oral contraceptive (combination of ethinyl estradiol and norethindrone).
Zoonotic diseases continue to present health, social, and economic challenges in China. While the country has demonstrated strong outbreak response capabilities, current efforts remain reactive and top-down. Shifting toward primary prevention at the human-animal-environment interface with enhanced risk communication offers a more sustainable approach to reducing zoonotic disease risks. This review synthesized peer-reviewed and gray literature in English and Chinese to characterize human-animal contact behaviors associated with 93 zoonotic diseases monitored by China's public health, agricultural, and forestry sectors. It examined contact pathways across key animal groups known to carry zoonotic pathogens, identified human populations at risk, and analyzed the demographic, socio-cultural, and ecological factors shaping these contacts. Focusing on four major human-animal interfaces, the review further identified lessons and best practices for effective risk communications. Findings reveal that human-animal contact in China is diverse and embedded in daily routines, cultural practices, and economic activities, with distinct risk profiles presented across animal groups and socio-ecological settings. Populations such as smallholder farmers, herders, rural residents, market vendors, and workers in informal sectors face higher exposure risk, influenced by socio-economic conditions and ecological changes. Gaps remain in surveillance of informal practices, emerging pathogens, and behavioral data. Evidence from global and local experiences highlights the value of behavior-centered, community-engaged communication grounded in One Health principles, emphasizing participatory design, culturally relevant education, local leadership, and integration with public service systems. Overall, this review provides an integrated understanding of zoonotic disease risks and prevention opportunities from social-behavioral and communication perspectives. It identified priority populations, settings, and best practices for targeted and effective strategies, underscoring the need for coordinated One Health efforts to address complex human-animal-environment interactions and promote proactive zoonotic disease prevention in China and beyond.
Numerous phylogenetic comparative studies have attempted to explain differences in species phenotypes as a function of present-day social or physical environments. In some cases, these investigations have also shown dramatic convergences among distantly related species, illustrating how common selection pressures can produce repeated adaptation. In other cases, distantly related species exhibit phenotypes that appear quite different, and despite putative similarities in selection. Understanding why this is the case is central to our broader knowledge of how evolution operates in nature, and what accounts for the diversity we see in the natural world. Species responding differently to common selection pressures likely traces back to mechanistic constraints and evolutionary starting points bounding the direction of adaptation. These are common themes in evolutionary biology and there is a long history dating back to Lorenz and Tinbergen of considering "proximate" and "ultimate" factors for interpreting present-day behavior. Yet, integration of mechanism and history is still often missing from behavioral ecology. This might reflect the reasonable assumption that behavior is, for the most part, shaped through plasticity or adaptive evolution in response to conditions existing in present-day environments. Yet this assumption fails to explain why species in similar environments so often differ in behavior. Only when behavior is placed into its broader phylogenetic context and explored through the lens of "paths-of-least-resistance" does adaptive innovation even become apparent. Iconic case studies including the sword of swordtails, the song of Darwin finches and the dewlap of anole lizards are examined through this lens to reveal hidden trajectories of adaptation that have led to innovation and diversity.
Low divergence optical beams propagating through the atmospheric surface layer (ASL) are significantly impacted by turbulence effects. A primary challenge for free-space optical communications and directed energy systems is variability in pointing requirements. Angle-of-arrival (AoA) measurements of a low divergence optical beam that is propagated across a 16 km path across the Chesapeake Bay, taken over a 1-month period, reveal a pattern of consistent, predictable AoA under unstable meteorological conditions that transition to larger and more highly variable AoA under neutral and stable conditions. It is shown that, for a given refractive index structure parameter (Cn2), in stable conditions, the expected AoA can vary by up to 1 mrad, compared to less than 0.1 mrad variation in unstable conditions. Meteorological data on the Chesapeake Bay propagation path are input to the Navy Atmospheric Vertical Surface Layer Model (NAVSLaM), and beams are ray-traced through the resulting simulated refractivity profiles, with account being taken for earth curvature. The resulting AoA are evaluated and show the same angular range as the observations but, while ducting conditions are predicted for highly stable conditions, the model does not explain the instability in pointing seen at the onset of stable conditions. The strong correlation between air and water temperature difference (AWTD) and stability conditions in maritime environments provides a practical use for indicating the expected accuracy of beam pointing in the field.
Hyperspectral remote sensing images provide rich spatial and spectral information about the Earth's surface, making them an essential tool for Earth observation. However, existing spaceborne hyperspectral payloads experience slow acquisition speeds and generate large data volumes, posing significant challenges for real-time applications. Moreover, the complex optical design and relatively high cost of traditional hyperspectral payloads hinder their broad-scale in-orbit deployment. In this work, we have proposed and completed the world's first computational imaging-enabled compact spaceborne snapshot compressive hyperspectral payload, named BUPT-spectra01, which was successfully launched on November 11, 2024, at the Jiuquan Satellite Launch Center in China. We design a reflective coding structure, which enables BUPT-spectra01 to achieve high compactness (182 mm × 214 mm × 94 mm, 1.535 kg) and low cost. The payload operates in a sun-synchronous orbit at an altitude of 520 km, with a ground imaging swath width of 51 km by 64 km. Through a single exposure (1 ms), the payload enables 47-band hyperspectral imaging with a spectral resolution of 6.5 nm, achieving 47-times data compression simultaneously. To achieve high-accuracy hyperspectral information reconstruction, we design a novel spatial-spectral inference neural network (SSI-Net). Moreover, BUPT-spectra01 can image at a rate of 30 frames per second, which allows video-level hyperspectral observation. In-orbit experiments demonstrate that BUPT-spectra01 achieves accurate classification of ground cover based on hyperspectral features, showing promise in hyperspectral observation applications such as disaster management, environment monitoring, and resource exploration. This breakthrough significantly advances the application of computational imaging in aerospace observation, contributing to the progress of future satellite internet.
The presence of artificial structures in our marine environments is increasing rapidly, with negative impacts for biodiversity. Greening of grey infrastructure (GGI) - an eco-engineering method applied to the marine context - aims to increase the ecological value of traditional grey infrastructure, while still allowing it to perform its primary human-centric function. GGI is a rapidly increasing field of research, being tested and implemented worldwide by academics, private practitioners, governments, non-governmental organisations (NGOs), amongst others, using a variety of methods. Outcomes vary widely, and results are communicated across a range of peer-reviewed and grey literature, rendering the evidence base for the effectiveness of GGI fragmented. To inform future decision-making regarding GGI application, it is critical to consolidate and evaluate existing research. To do so, we propose a systematic review and meta-analysis that will answer the following primary question: "What are the effects of GGI interventions applied to marine structures on the diversity, abundance, biomass, composition, and functional diversity of species on or around these ecologically enhanced structures?". Additionally, we will answer a series of secondary questions relating to intervention type, material use, geographic variations and other relevant associated variables. This systematic review will follow the Collaboration for Environmental Evidence Guidelines and Standards for Evidence Synthesis in Environmental Management. Using a defined search string, literature searches will be run in English in at least five databases, three repositories and 10 websites, gathering both peer-reviewed and grey literature. Returns will be screened at title, abstract, and full text levels against defined inclusion criteria. Relevant metadata and effect data will be extracted from each study and used to write a narrative review and, where data allow, a meta-analysis of quantified effects. This review will provide a robust, up to date, consolidated and evaluated evidence base to inform future decision-making regarding the implementation of greening of grey infrastructure methods.
Extensive wildfire profoundly influences Earth system feedback and can drive major ecosystem disturbances, yet its timing and role in the Late Devonian Frasnian-Famennian (F-F) mass extinction remain unclear. To determine whether wildfires were a driver or consequence of contemporaneous oceanic anoxic events (OAEs), we present a high temporal resolution multiproxy record (biomarkers, microfossils, and trace metals) from the Chattanooga Shale in the southeastern United States. Pyrogenic PAHs and inertinite macerals increased after peaks in δ13Corg and redox-sensitive proxies, indicating that wildfire activity intensified in response to post-OAEs oxygen rise rather than triggering anoxia. Modeling of global δ13C records reveals that the lag between OAEs/organic carbon burial and wildfire onset reflects the time required for atmospheric oxygen to accumulate to levels sustaining widespread combustion. Together, these results provide the first high-resolution dataset capable of resolving the temporal sequence between OAEs and wildfire activity, enabling the establishment of their causal linkage during the catastrophic F-F environmental disruptions.
Resilience analysis is crucial for developing interventions that mitigate riverine flood risk in the context of global environmental change and sustainable development. Index-based assessment remains the dominant methodological approach for operationalizing and evaluating resilience. Nevertheless, current frameworks often rely on subjective indicators, lack statistical validation, and exhibit limited utility for environmental planning. This study proposes an integrated methodology that combines expert-based multi-criteria decision analysis, data-driven principal axis factoring, and Monte Carlo-based index construction with deep learning-enabled predictive validation, supported by spatially explicit, explainable artificial intelligence (XAI). Using 1000 georeferenced observations and 56 indicators spanning socio-cultural, economic, physical-infrastructural, organizational-institutional, hydraulic, and ecological domains for 49 unions in the Brahmaputra River Basin (Bangladesh), the triadic framework yields a fully validated Combined Multidimensional Resilience Index (CMRI) that offers a transparent, rigorous, and statistically robust characterization of community-level resilience. The CMRI reveals significant spatial differences: low-resilience conditions are heavily concentrated in northern floodplains (70% of low-resilience locations), whereas higher resilience is predominantly observed in central and southern unions (56%). The deep learning model (DR-DNN) demonstrates excellent predictive skill (AUC = 0.989; 87.8% accuracy), confirming the internal coherence and predictive learnability of resilience structures. SHAP-based explanations highlight hydraulic deficits, ecological degradation, and institutional weaknesses as primary drivers of vulnerability. In contrast, ecological integrity, institutional capacity, financial inclusion, and reduced social vulnerability emerge as key determinants of high resilience. The asymmetric vulnerability-resilience pathways indicate that addressing deficits alone is insufficient; targeted ecological restoration and institutional strengthening are required to raise resilience levels. By integrating rigorous quantitative modeling with interpretable XAI, this study advances flood-resilience assessment from a static diagnostic exercise to a dynamic, actionable, and context-sensitive decision-support framework, providing a scalable model for rural and transboundary riverine floodplain systems.
When manipulating objects, our brain continuously adjusts grip force (GF) to the variations in load force (LF) generated by our own movements. GF-LF coordination provides a window into the predictive capabilities of the brain. To better understand how gravity influences these predictions, we analyzed grip dynamics and movement kinematics in astronauts (two females, nine males) manipulating objects on the ground (in 1G) and during spaceflight in a stable weightless environment (in 0G). We found that the imprint of gravity remains visible in the way we manipulate objects even after months of living in weightlessness. Empirical evidence showed that humans overcompensate for the absence of weight when manipulating objects in 0G, suggesting an anti-Bayesian anticipation of object buoyancy or negative weight. Shortly after returning to Earth, progressive kinematic adjustments were observed during the first movements with the object, as well as signs of incorrect LF predictions. The gradual and incomplete adjustments when passing from one gravitational context to the other underlines the predictive nature of the neural processes underlying these behaviors. In addition, a detailed examination of GF in weightlessness revealed a heretofore unrecognized link between the parameters of the GF/LF coupling, best described by a quadratic dependence of GF on both LF and the kinetic energy of the object. We conclude that not only is the risk of slip a determining factor in the control strategy, the impact of potential accidental slips is important as well.