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Effective nitrogen fertilizer management is crucial for reducing nitrous oxide (N2O) emissions while ensuring food security within planetary boundaries. However, climate change might also interact with management practices to alter N2O emission and emission factors (EFs), adding further uncertainties to estimating mitigation potentials. Here, we developed a new hybrid modeling framework that integrates a machine learning model with an ensemble of eight process-based models to project EFs under different climate and nitrogen policy scenarios. Our findings reveal that EFs are dynamically modulated by environmental changes, including climate, soil properties, and nitrogen management practices. Under low-ambition nitrogen regulation policies, EF would increase from 1.18%-1.22% in 2010 to 1.27%-1.34% by 2050, representing a relative increase of 4.4%-11.4% and exceeding the IPCC tier-1 EF of 1%. This trend is particularly pronounced in tropical and subtropical regions with high nitrogen inputs, where EFs could increase by 0.14%-0.35% (relative increase of 11.9%-17%). In contrast, high-ambition policies have the potential to mitigate the increases in EF caused by climate change, possibly leading to slight decreases in EFs. Furthermore, our results demonstrate that global EFs are expected to continue rising due to warming and regional drying-wetting cycles, even in the absence of changes in nitrogen management practices. This asymmetrical influence of nitrogen fertilizers on EFs, driven by climate change, underscores the urgent need for immediate N2O emission reductions and further assessments of mitigation potentials. This hybrid modeling framework offers a computationally efficient approach to projecting future N2O emissions across various climate, soil, and nitrogen management scenarios, facilitating socio-economic assessments and policy-making efforts.

Nitrous oxide (N2O) is a long-lived potent greenhouse gas and stratospheric ozone-depleting substance that has been accumulating in the atmosphere since the preindustrial period. The mole fraction of atmospheric N2O has increased by nearly 25 % from 270 ppb (parts per billion) in 1750 to 336 ppb in 2022, with the fastest annual growth rate since 1980 of more than 1.3 ppb yr−1 in both 2020 and 2021. According to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR6), the relative contribution of N2O to the total enhanced effective radiative forcing of greenhouse gases was 6.4 % for 1750–2022. As a core component of our global greenhouse gas assessments coordinated by the Global Carbon Project (GCP), our global N2O budget incorporates both natural and anthropogenic sources and sinks and accounts for the interactions between nitrogen additions and the biogeochemical processes that control N2O emissions. We use bottom-up (BU: inventory, statistical extrapolation of flux measurements, and process-based land and ocean modeling) and top-down (TD: atmospheric measurement-based inversion) approaches. We provide a comprehensive quantification of global N2O sources and sinks in 21 natural and anthropogenic categories in 18 regions between 1980 and 2020. We estimate that total annual anthropogenic N2O emissions have increased 40 % (or 1.9 Tg N yr−1) in the past 4 decades (1980–2020). Direct agricultural emissions in 2020 (3.9 Tg N yr−1, best estimate) represent the large majority of anthropogenic emissions, followed by other direct anthropogenic sources, including fossil fuel and industry, waste and wastewater, and biomass burning (2.1 Tg N yr−1), and indirect anthropogenic sources (1.3 Tg N yr−1) . For the year 2020, our best estimate of total BU emissions for natural and anthropogenic sources was 18.5 (lower–upper bounds: 10.6–27.0) Tg N yr−1, close to our TD estimate of 17.0 (16.6–17.4) Tg N yr−1. For the 2010–2019 period, the annual BU decad

First-shot (FS) unsupervised anomalous sound detection (ASD) is a brand-new task introduced in DCASE 2023 Challenge Task 2, where the anomalous sounds for the target machine types are unseen in training. Existing methods often rely on the availability of normal and abnormal sound data from the target machines. However, due to the lack of anomalous sound data for the target machine types, it becomes challenging when adapting the existing ASD methods to the first-shot task. In this paper, we propose a new framework for the first-shot unsupervised ASD, where metadata-assisted audio generation is used to estimate unknown anomalies, by utilising the available machine information (i.e., metadata and sound data) to fine-tune a text-to-audio generation model for generating the anomalous sounds that contain unique acoustic characteristics accounting for each different machine type. We then use the method of Time-Weighted Frequency domain audio Representation with Gaussian Mixture Model (TWFR-GMM) as the backbone to achieve the first-shot unsupervised ASD. Our proposed FS-TWFR-GMM method achieves competitive performance amongst top systems in DCASE 2023 Challenge Task 2, while requiring only 1% model parameters for detection, as validated in our experiments.

Self-supervised learning methods have achieved promising performance for anomalous sound detection (ASD) under domain shift by incorporating the metadata of domain shift types and machine sound attributes in feature learning. However, the relation between domain shifts and machine sound attributes has yet to be fully utilised despite their potential benefits for characterising domain shifts. This paper presents a hierarchical metadata information constrained self-supervised ASD method, where the hierarchical relation between domain shift types (section IDs) and attributes is constructed and used as constraints to improve feature representation. In addition, we propose an attribute-group-centre based method for calculating the anomaly score under the domain shift condition. Experiments show improved audio feature learning over the state-of-the-art methods in DCASE 2022 challenge Task 2.

By incorporating two different fracture mechanisms and salient unilateral effects in rock materials, we propose a thermomechanical phase-field model to capture thermally induced fracture and shear heating in the process of rock failure. The heat conduction equation is derived, from which the plastic dissipation is treated as a heat source. We then ascertain the effect of the non-associated plastic flow on frictional dissipation and show how it improves the predictive capability of the proposed model. Taking advantage of the multiscale analysis, we propose a phase-field-dependent thermal conductivity with considering the unilateral effect of fracture. After proposing a robust algorithm for solving involved three-field coupling and damage-plasticity coupling problems, we present three numerical examples to illustrate the abilities of our proposed model in capturing various thermo-mechanically coupled behaviors.

Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90g% higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2Combining double low line0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

Wildfire is an important ecosystem process, influencing land biogeophysical and biogeochemical dynamics and atmospheric composition. Fire-driven loss of vegetation cover, for example, directly modifies the surface energy budget as a consequence of changing albedo, surface roughness, and partitioning of sensible and latent heat fluxes. Carbon dioxide and methane emitted by fires contribute to a positive atmospheric forcing, whereas emissions of carbonaceous aerosols may contribute to surface cooling. Process-based modeling of wildfires in Earth system land models is challenging due to limited understanding of human, climate, and ecosystem controls on fire counts, fire size, and burned area. Integration of mechanistic wildfire models within Earth system models requires careful parameter calibration, which is computationally expensive and subject to equifinality. To explore alternative approaches, we present a deep neural network (DNN) scheme that surrogates the process-based wildfire model with the Energy Exascale Earth System Model (E3SM) interface. The DNN wildfire model accurately simulates observed burned area with over 90g% higher accuracy with a large reduction in parameterization time compared with the current process-based wildfire model. The surrogate wildfire model successfully captured the observed monthly regional burned area during validation period 2011 to 2015 (coefficient of determination, R2Combining double low line0.93). Since the DNN wildfire model has the same input and output requirements as the E3SM process-based wildfire model, our results demonstrate the applicability of machine learning for high accuracy and efficient large-scale land model development and predictions.

Background: Adolescents' daily active travel (AT) has positive health effects. However, previous studies had paid little attention on the differences between the streetscape characteristics-AT relationship on the weekdays and weekends, and most of them only focused on the environmental exposure around home and schools. Methods: This study used data from a survey (N = 476) in Guangzhou, China between April and September 2018 and streetscape images from the Tencent Map. By using the Hurdle models and the Tobit models, we examined the associations between the streetscape characteristics and AT patterns (i.e., frequency and duration) among adolescents on weekdays and weekend days. Results: Streetscape greenery was negatively associated with odds of AT and positively associated with the number of AT trips on weekdays, while it was negatively related to AT duration on weekends. Pavement ratio and traffic volume were negatively associated with odds of AT and AT duration on weekdays. Safety facilities and street vitality were positively associated with the AT frequency and AT duration on weekends and weekdays. Our stratified analyses indicated that gender is an effect modifier of the built environment-AT patterns associations. Conclusion: The results showed that the relationships between streetscape characteristics and AT patterns differed between weekdays and weekends. Streetscape characteristics related to the odds of AT were not necessarily the same as those associated with AT intensity. These findings offer practical guidance for creating sustainable cities in China through AT promotion.

Promoting adolescents’ active school travel may support their health and wellbeing. Based on survey data of 473 adolescents in Guangzhou, China, and street view images, we employed generalized structural equation models to examine 1) the relationships between objective and perceived streetscape characteristics and adolescents’ active school travel and 2) the mediating roles of perceived safety, walkability, and air pollution. Results showed that street safety, vitality, greenery, and vehicle volume were positively related to odds of active school travel, while higher pavement ratio was associated with lower odds of active school travel. Street safety, pavement ratio, and vehicle volume were related to active school travel through perceived walkability, while street vitality and vehicle volume were associated with active school travel through the perceived safety-walkability path and perceived air pollution-walkability path. Our findings provide practical insights that could help city planners to make urban street environments more child-friendly for adolescents’ active travelling.

Phthalate esters (PAEs), which are commonly used as plasticizers in food processing and packaging, are common pollutants. Dietary intake is the primary route to human exposure to PAEs. Data show that retail packaged and cooked foods are most likely to be contaminated with PAEs, indicating that the food preparation and cooking processes may contribute to the migration and transformation of PAEs in food. In this review, we collected the latest investigations on the occurrence of PAEs in different food categories, discussed the mechanisms of the fate of PAEs during cooking, and summarized the reduction measures of PAEs in food. Foods such as water and beverages, meat, condiments, and baby foods are linked to PAE exposure. Many cooking techniques and procedures, including steaming, boiling, microwave heating, toasting, frying, and packaging, are responsible for the occurrence, migration, and transformation of PAEs in foods. The occurrence, transformation, and migration of PAEs in food are influenced by a variety of factors, including food categories, the cooking process, packaging materials, and physicochemical properties of PAEs. There are several ways to prevent or reduce PAEs in food, including the selection of safe packaging materials, proper food categories, cooking methods, and replacement of PAEs

This paper presents a robust finite-horizon control scheme based on data that produces feasible control sequences. The scheme makes use of a database that includes information from prior experiences of the same and others controllers handling similar systems. By the convex combination of feasible histories plus an auxiliary control law that deals with uncertainties, this scheme can be used as a robust historian-based predictive controller. Further application could include a cooperative learning-based strategy in which multiple controllers share their previous executions to gain collective benefits in terms of performance. The validity of the proposed controller is tested in a simulated case study.

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters.

This paper presents a robust finite-horizon control scheme based on data that produces feasible control sequences. The scheme makes use of a database that includes information from prior experiences of the same and others controllers handling similar systems. By the convex combination of feasible histories plus an auxiliary control law that deals with uncertainties, this scheme can be used as a robust historian-based predictive controller. Further application could include a cooperative learning-based strategy in which multiple controllers share their previous executions to gain collective benefits in terms of performance. The validity of the proposed controller is tested in a simulated case study.

Immersive and spatial sound reproduction has been widely studied using loudspeaker arrays. However, flat-panel loudspeakers that utilize thin flat panels with force actuators are a promising alternative to traditional coaxial loudspeakers for practical applications, with benefits in low-visual profiles and diffuse radiation. Literature has addressed the sound quality and applications of flat-panel loudspeakers in three-dimensional sound reproduction, such as wave field synthesis and sound zones. This paper revisits the spatial sound perception of flat-panel loudspeakers, specifically the localization mismatch between the perceived and desired sound directions when using amplitude panning. Subjective tests in an anechoic chamber with 24 subjects result in the mean azimuth direction mismatch within ±6.0° and the mean elevation mismatch within ±10.0°. The experimental results show that the virtual source created by amplitude panning over a flat-panel loudspeaker still achieves spatial localization accuracy close to that of a real sound source, despite not using complex algorithms or acoustic transfer function information. The findings of this study establish a benchmark for virtual source localization in spatial sound reproduction using flat-panel loudspeakers, which can serve as a starting point for future research and optimization of algorithms.

State-of-the-art audio captioning methods typically use the encoder-decoder structure with pretrained audio neural networks (PANNs) as encoders for feature extraction. However, the convolution operation used in PANNs is limited in capturing the long-time dependencies within an audio signal, thereby leading to potential performance degradation in audio captioning. This letter presents a novel method using graph attention (GraphAC) for encoder-decoder based audio captioning. In the encoder, a graph attention module is introduced after the PANNs to learn contextual association (i.e. the dependency among the audio features over different time frames) through an adjacency graph, and a top-k mask is used to mitigate the interference from noisy nodes. The learnt contextual association leads to a more effective feature representation with feature node aggregation. As a result, the decoder can predict important semantic information about the acoustic scene and events based on the contextual associations learned from the audio signal. Experimental results show that GraphAC outperforms the state-of-the-art methods with PANNs as the encoders, thanks to the incorporation of the graph attention module into the encoder for capturing the long-time dependencies within the audio signal.

This paper presents a robust finite-horizon control scheme based on data that produces feasible control sequences. The scheme makes use of a database that includes information from prior experiences of the same and others controllers handling similar systems. By the convex combination of feasible histories plus an auxiliary control law that deals with uncertainties, this scheme can be used as a robust historian-based predictive controller. Further application could include a cooperative learning-based strategy in which multiple controllers share their previous executions to gain collective benefits in terms of performance. The validity of the proposed controller is tested in a simulated case study.

The European Union has some dedicated tools and mechanisms available to respond to natural hazard events including the European Union Solidarity Fund (EUSF). It follows the objective of granting financial assistance to Member States in the event of a major disaster with serious consequences. In the latest EU long-term budget plan—the Multiannual Financial Framework 2021–2027—the EUSF was merged with the Emergency Aid Reserve (EAR) to form the new Solidarity and Emergency Aid Reserve (SEAR). One additional significant change was made in 2020 which saw an extension of the scope of the EUSF. This extension allowed the EUSF to cover losses incurred due to major public health emergencies such as the COVID-19 pandemic. It is therefore now a multi-hazard and multi-risk financing instrument designed to financially assist during the emergency phase in case of an emergency event. We assess the consequences of these changes in the light of potential advantages as well as disadvantages compared to the prior EUSF structure. The results will be used to provide some policy recommendations as to how to move forward with the identified challenges. We especially recommend separating the EUSF from the coverage of large-scale public health emergencies and the emergencies covered by the EAR. Instead, we suggest establishing a new flexibility instrument that covers emergencies such as public health related ones as well as the ones within the EAR. The analysis gives some important insights, scientific as well as policy wise, about advantages as well as limitations of financial instruments that simultaneously should tackle different types of hazards and risks.

The European Union has some dedicated tools and mechanisms available to respond to natural hazard events including the European Union Solidarity Fund (EUSF). It follows the objective of granting financial assistance to Member States in the event of a major disaster with serious consequences. In the latest EU long-term budget plan—the Multiannual Financial Framework 2021–2027—the EUSF was merged with the Emergency Aid Reserve (EAR) to form the new Solidarity and Emergency Aid Reserve (SEAR). One additional significant change was made in 2020 which saw an extension of the scope of the EUSF. This extension allowed the EUSF to cover losses incurred due to major public health emergencies such as the COVID-19 pandemic. It is therefore now a multi-hazard and multi-risk financing instrument designed to financially assist during the emergency phase in case of an emergency event. We assess the consequences of these changes in the light of potential advantages as well as disadvantages compared to the prior EUSF structure. The results will be used to provide some policy recommendations as to how to move forward with the identified challenges. We especially recommend separating the EUSF from the coverage of large-scale public health emergencies and the emergencies covered by the EAR. Instead, we suggest establishing a new flexibility instrument that covers emergencies such as public health related ones as well as the ones within the EAR. The analysis gives some important insights, scientific as well as policy wise, about advantages as well as limitations of financial instruments that simultaneously should tackle different types of hazards and risks.

The European Union has some dedicated tools and mechanisms available to respond to natural hazard events including the European Union Solidarity Fund (EUSF). It follows the objective of granting financial assistance to Member States in the event of a major disaster with serious consequences. In the latest EU long-term budget plan—the Multiannual Financial Framework 2021–2027—the EUSF was merged with the Emergency Aid Reserve (EAR) to form the new Solidarity and Emergency Aid Reserve (SEAR). One additional significant change was made in 2020 which saw an extension of the scope of the EUSF. This extension allowed the EUSF to cover losses incurred due to major public health emergencies such as the COVID-19 pandemic. It is therefore now a multi-hazard and multi-risk financing instrument designed to financially assist during the emergency phase in case of an emergency event. We assess the consequences of these changes in the light of potential advantages as well as disadvantages compared to the prior EUSF structure. The results will be used to provide some policy recommendations as to how to move forward with the identified challenges. We especially recommend separating the EUSF from the coverage of large-scale public health emergencies and the emergencies covered by the EAR. Instead, we suggest establishing a new flexibility instrument that covers emergencies such as public health related ones as well as the ones within the EAR. The analysis gives some important insights, scientific as well as policy wise, about advantages as well as limitations of financial instruments that simultaneously should tackle different types of hazards and risks.

The European Union has some dedicated tools and mechanisms available to respond to natural hazard events including the European Union Solidarity Fund (EUSF). It follows the objective of granting financial assistance to Member States in the event of a major disaster with serious consequences. In the latest EU long-term budget plan—the Multiannual Financial Framework 2021–2027—the EUSF was merged with the Emergency Aid Reserve (EAR) to form the new Solidarity and Emergency Aid Reserve (SEAR). One additional significant change was made in 2020 which saw an extension of the scope of the EUSF. This extension allowed the EUSF to cover losses incurred due to major public health emergencies such as the COVID-19 pandemic. It is therefore now a multi-hazard and multi-risk financing instrument designed to financially assist during the emergency phase in case of an emergency event. We assess the consequences of these changes in the light of potential advantages as well as disadvantages compared to the prior EUSF structure. The results will be used to provide some policy recommendations as to how to move forward with the identified challenges. We especially recommend separating the EUSF from the coverage of large-scale public health emergencies and the emergencies covered by the EAR. Instead, we suggest establishing a new flexibility instrument that covers emergencies such as public health related ones as well as the ones within the EAR. The analysis gives some important insights, scientific as well as policy wise, about advantages as well as limitations of financial instruments that simultaneously should tackle different types of hazards and risks.

This paper presents a robust finite-horizon control scheme based on data that produces feasible control sequences. The scheme makes use of a database that includes information from prior experiences of the same and others controllers handling similar systems. By the convex combination of feasible histories plus an auxiliary control law that deals with uncertainties, this scheme can be used as a robust historian-based predictive controller. Further application could include a cooperative learning-based strategy in which multiple controllers share their previous executions to gain collective benefits in terms of performance. The validity of the proposed controller is tested in a simulated case study.

Because of the smeared representation of phase-field fracture, reconstructing lower-dimensional crack paths is challenging, which may impede the further applications of the phase-field method in the modeling of fault friction and fluid flow in fracture. In the present work, we propose to capture the crack curves or surfaces from two-dimensional or three-dimensional phase-field point cloud using an optimized ridge regression algorithm, and k-nearest neighbor and principal component analysis are used to estimate the normal direction of each segment on the identified discrete crack path. The sensitivity and computational efficiency of the proposed method are investigated thoroughly. Subsequently, this method is extended to reconstruct the complex discrete fracture networks. Finally, several benchmarks for fracture, fault friction and fluid flow problems are presented to demonstrate the strength of the proposed approach.

Organophosphate flame retardants (OPFRs) are found in various environmental matrixes and human samples. Exposure to OPFRs during gestation may interfere with pregnancy, for example, inducing maternal oxidative stress and maternal hypertension during pregnancy, interfering maternal and fetal thyroid hormone secretion and fetal neurodevelopment, and causing fetal metabolic abnormalities. However, the consequences of OPFR exposure on pregnant women, impact on mother-to-child transmission of OPFRs, and harmful effects on fetal and pregnancy outcomes have not been evaluated. This review describes the exposure to OPFRs in pregnant women worldwide, based on metabolites of OPFRs (mOPs) in urine for prenatal exposure and OPFRs in breast milk for postnatal exposure. Predictors of maternal exposure to OPFRs and variability of mOPs in urine have been discussed. Mother-to-child transmission pathways of OPFRs have been scrutinized, considering the levels of OPFRs and their metabolites in amniotic fluid, placenta, deciduae, chorionic villi, and cord blood. The results showed that bis(1,3-dichloro-2-propyl) phosphate (BDCIPP) and diphenyl phosphate (DPHP) were the two predominant mOPs in urine, with detection frequencies of >90%. The estimated daily intake (EDIM) indicates low risk when infants are exposed to OPFRs from breast milk. Furthermore, higher exposure levels of OPFRs in pregnant women may increase the risk of adverse pregnancy outcomes and influence the developmental behavior of infants. This review summarizes the knowledge gaps of OPFRs in pregnant women and highlights the crucial steps for assessing health risks in susceptible populations, such as pregnant women and fetuses.

Direct reduction of combusted iron powders using renewable energy is one of the keys to ensure a carbon-neutral iron fuel cycle. Based on (de-)fluidization experimental study in Part I, reduction experiments of micron-sized combusted iron powders using hydrogen in a lab-scale gas-fluidized bed are conducted at temperatures ranging from 500 ∘C to 650 ∘C. The effects of temperature, hydrogen velocity and concentration on the reduction performance (including reduction degree, particle morphology and size change) and sintering/defluidization behavior are investigated. Overall, a reduction degree higher than 90% is reached at a lower temperature of 500 ∘C, whereas at higher temperatures the serious problem of particle sintering is encountered, which leads to quick defluidization and thus a relatively low reduction degree. Temperature has been found to have a pronounced effect on the sintering and reduction behavior, while the effect of gas velocity on the reduction process is negligible. SEM images show that the reduced powders are porous with the pore size increasing with temperature. These experimental findings provide insights for the design of industrial process of hydrogen-based regeneration of iron fuels.

Hydrogen-based direct reduction of iron oxide powder in gas-fluidized beds has become an emerging technology. Agglomeration of fine powder at elevated temperatures is however one key issue hindering the technological development. To obtain better knowledge of the high-temperature agglomeration mechanism, the (de-)fluidization behavior of micron-sized combusted iron particles is experimentally studied in this Part I. Effects of various operating and material variables (temperature, gas flow rate, and particle size) on the fluidization and agglomeration behavior are investigated. Different fluidization regimes (e.g. stable fluidization, agglomerating fluidization, fast defluidization) are categorized based on the experimental results. A theoretical model based on the force balance is developed to predict the regime boundaries of fluidization. The obtained knowledge is applied for experimental design of the hydrogen-based iron direct reduction process in Part II.

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters.

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters.

Fiscal resilience against disasters is vital for the recovery in the aftermath of climate hazards. Without swift access to available funds for disaster relief, damages to human and the economy would be further exacerbated. How insurance may influence fiscal performance over time and can increase fiscal resilience for today and under a future climate has not been looked at yet in detail. Focusing on the Caribbean region and on the fiscal performance of governments after disaster events, we empirically analyze the effectiveness of the Caribbean Catastrophe Risk Insurance Facility (CCRIF) regarding the reduction of short-term fiscal effects. We embed this analysis within a novel climate impact storyline approach where we produce past plausible events and investigate the usefulness of insurance under such events. The storylines were modified according to global and climate change related boundary conditions to address the issue whether the CCRIF is fit for purpose or will need to be adapted in the future. We found that both hurricane strikes and the CCRIF affect fiscal outcomes of Caribbean countries. Furthermore, there are indications that CCRIF can counteract the negative fiscal consequences over the short term period induced by the disaster. Our analysis should shed some light on the current discussions on how development related assistance can be structured to enhance climate resilience in highly exposed countries for both direct and fiscal impacts of disasters.

In stereoscope-based minimally invasive surgeries (MISs), dense stereo matching plays an indispensable role in 3-D shape recovery, AR, VR, and navigation tasks. Although numerous deep neural network (DNN) approaches are proposed, the conventional prior-free approaches are still popular in the industry because of the lack of open-source annotated dataset and the limitation of the task-specific pretrained DNNs. Among the prior-free stereo matching algorithms, there is no successful real-time algorithm in none GPU environment for MIS. This article proposes the first CPU-level real-time prior-free stereo matching algorithm for general MIS tasks. We achieve an average 14-17 Hz on 640 × 480 images with a single-core CPU (i5-9400) for surgical images. Meanwhile, it achieves slightly better accuracy than the popular efficient large-scale stereo matching (ELAS) method. The patch-based fast disparity searching algorithm is adopted for the rectified stereo images. A coarse-to-fine Bayesian probability and a spatial Gaussian mixed model were proposed to evaluate the patch probability at different scales. An optional probability density function estimation algorithm was adopted to quantify the prediction variance. Extensive experiments demonstrated the proposed method's capability to handle ambiguities introduced by the textureless surfaces and the photometric inconsistency from the non-Lambertian reflectance and dark illumination. The estimated probability managed to balance the confidences of the patches for stereo images at different scales. It has similar or higher accuracy and fewer outliers than the baseline ELAS in MIS, while it is 4-5 times faster.

Scientific communities are increasingly publishing data to evaluate, accredit, and build on published research. However, guidelines for curating data for publication are sparse for model-related research, limiting the usability of archived simulation data. In particular, there are no established guidelines for archiving data related to terrestrial models that simulate land processes and their coupled interactions with climate. Terrestrial modelers have a unique set of challenges when publishing data due to the diversity of scientific domains, research questions, and the types and scales of simulations. Researchers in the U.S. Department of Energy’s (DOE) projects use a variety of multiscale models to advance robust predictions of terrestrial and subsurface ecosystem processes. Here, we synthesize archiving needs for data associated with different DOE models, and provide guidelines for publishing terrestrial model data components following FAIR (Findable, Accessible, Interoperable, Reusable) principles. The guidelines recommend archiving model inputs and testing data used in final simulation runs along with associated codes, workflow scripts, and metadata in public repositories. Researchers should consider archiving model outputs if they are within the storage limits of the repository. We also provide considerations for how to bundle files into different data publications with citable digital object identifiers. Finally, we identify repository features and tools that would enable storage and reuse of model data. Given the diversity of DOE terrestrial models, these guidelines are transferable to other model types and will enable efficient reuse of simulation data for purposes such as model intercomparisons, initialization, benchmarking, synthesis, and comparisons with field observations.

Tundra ecosystems have experienced an increased frequency of fire, and this trend is predicted to continue throughout the 21st Century. Post-fire recovery is underpinned by complex interactions between microbial functional groups that drive nutrient cycling. Here we use a mechanistic model to demonstrate an acceleration of the nitrogen cycle post-fire driven by changes in niche space and microbial competitive dynamics. We show that over the first 5-years post-fire, fast-growing bacterial heterotrophs colonize regions of the soil previously occupied by slower-growing saprotrophic fungi. The bacterial heterotrophs mineralize organic matter, releasing nutrients into the soil. This pathway outweighs new sources of nitrogen and facilitates the recovery of plant productivity. We broadly show here that while consideration of distinct microbial metabolisms related to carbon and nutrient cycling remains rare in terrestrial ecosystem models, they are important when considering the rate of ecosystem recovery post-disturbance and the feedback to soil nutrient cycles on centennial timescales.