Scale Mismatch Research Articles

Co-encapsulation of organic polymers and inorganic superparamagnetic iron oxide colloidal crystals requires matched diffusion time scales.

Nanoparticles (NPs) that contain both organic molecules and inorganic metal or metal oxide colloids in the same NP core are "composite nanoparticles" which are of interest in many applications, particularly in biomedicine as "theranostics" for the combined delivery of colloidal diagnostic imaging agents with therapeutic drugs. The rapid precipitation technique Flash NanoPrecipitation (FNP) enables continuous and scalable production of composite nanoparticles with hydrodynamic diameters between 40-200 nanometers (nm) that contain hydrophobic superparamagnetic iron oxide primary colloids. Composite NPs co-encapsulate these primary colloids (diameters of 6 nm, 15 nm, or 29 nm), a fluorescent dye (600 Daltons), and poly(styrene) homopolymer (1800, 50 000, or 200 000 Daltons) with NPs stabilized by a poly(styrene)-block-poly(ethylene glycol) (1600 Da-b-5000 Da) block copolymer. Nanoparticle assembly in FNP occurs by diffusion limited aggregation of the hydrophobic core components followed by adsorption of the hydrophobic block of the stabilizing polymer. The hydrodynamic diameter mismatch between the collapsed organic species and the primary colloids (0.5-5 nm versus 6-29 nm) creates a diffusion-aggregation time scale mismatch between components that can lead to nonstoichiometric co-encapsulation in the final nanoparticles; some nanoparticles are composites with primary colloids co-encapsulated alongside organics while others are devoid of the primary colloids and contain only organic species. We use a magnetic capture process to separate magnetic composite nanoparticles from organic-only nanoparticles and quantify the amount of iron oxide colloids and hydrophobic fluorescent dye (as a proxy for total hydrophobic polymer content) in the magnetic and nonmagnetic fractions of each formulation. Analysis of the microstructure in over 1100 individual nanoparticles by TEM imaging and composition measurements identifies the conditions that produce nonstoichiometric composite NP populations without co-encapsulated magnetic iron oxide colloids. Stoichiometric magnetically responsive composite NPs are produced when the ratio of characteristic diffusion-aggregation time scales between the inorganic primary colloid and the organic core component is less than 30 and all NPs in a dispersion contain organic and inorganic species in approximately the same ratio. These rules for assembly of colloids and organic components into homogeneous composite nanoparticles are broadly applicable.

Flexible multiscale cavity with omnidirectionality and high stability for in-site SERS detection of nanoplastics on oyster

Nanoplastics are widespread and pose a potential threat to human health. In this work, we designed a flexible multiscale cavity structured (MCS) surface-enhanced Raman scattering (SERS) substrate decorating Ni3S2 nanocavity on a modified polyvinylidene fluoride (PVDF) microcavity. The dense two-dimensional Ni3S2 nanosheets can provide a large surface area for the deposition of plasmonic silver nanoparticles (Ag NPs), increasing the density and strength of the “hotspots”. Additionally, the nanocavity (width ∼500 nm), formed by the nanosheet surroundings, offers not only electromagnetic enhancement of the cavity mode for SERS but also a crucial detection site for nanoplastics attachment. This effectively addresses the challenge of SERS detection due to the scale mismatch between nanoplastics (>50 nm) and conventional “hotspots” (<10 nm). Moreover, the flexible imprinted PVDF microcavity endows the proposed substrate with omnidirectional “hotspots” enhancement, ensuring the signal stability and the feasibility of in-site detection of the actual sample. As a result, we achieved the successful in-site detection of polystyrene (PS) spheres of different sizes on the surface of oysters, with a detection limit of 10-3 mg/mL for 50 nm diameter. The flexible multiscale cavity SERS substrate is expected to show great potential in food safety and environmental monitoring.

Self-Supervised Health Index Curve Generation for Condition-Based Predictive Maintenance

Modern machine degradation trend evaluation relies on the unsupervised model-based estimation of a health index (HI) from asset measurement data. This minimizes the need for timely human evaluation and avoids assumptions on the degradation shape. However, the comparability of multiple HI curves over time generated by unsupervised methods suffers from a scaling mismatch (non-coherent HIs) caused by the slightly different asset initial conditions and distinct HI model training. In this paper, we propose a novel self-supervised approach to obtain HI curves without suffering from the scale mismatch. Our approach uses an unsupervised autoencoder based on a convolutional neural network (CNN) to detect initial faults and autonomously label measurement samples. The resulting self-labeled data is used to train a 1D-CNN health predictor, effectively eliminating the scaling mismatch problem. On the basis of a bearing test-to-failure experiment, we show that our self-supervised scheme offers a promising solution for the non-coherent HI problem. In addition, we observed that our method indicates the gradual wear affecting the bearing prior to the independent analysis of a human expert.

Comprehensive quality assessment of satellite- and model-based soil moisture products against the COSMOS network in Germany

Critical to the reliable application of gridded soil moisture products is a thorough assessment of their quality at spatially compatible scales. While previous studies have attempted to evaluate different soil moisture products, comprehensive assessments at the appropriate scale remain challenging and rare. This study explores the potential of the Cosmic-Ray Soil Moisture Observation System (COSMOS) in Germany for effectively mitigating the scale mismatch between soil moisture products and reference measurements. A newly released extensive COSMOS data set provides time series of hectare-scale soil moisture of the main root zone at different locations in Germany and offers a unique opportunity for a comprehensive quality assessment of 15 commonly-used coarse-scale soil moisture products. Those are either satellite-based (AMSR2 LPRM, ASCAT H115/H116, Sentinel-1 SSM, SMAP L3E, SMOS L3, ASCAT/Sentinel-1 SWI, SMAP/Sentinel-1 L2, CCI Combined, and NOAA SMOPS) or model-based (ERA5-Land, GLDAS-Noah, ASCAT H141/H142, GLEAM, SMAP L4, and SMOS L4). We compared the temporal dynamics of the soil moisture products against that of the COSMOS soil moisture estimates at 21 sites of different land cover types over six years (2015–2020), including the drought of 2018. We found that the model-based products generally yield a higher correlation (0.74) and lower unbiased root-mean-square differences (0.05m3m−3) than the satellite-based products (0.60 and 0.07m3m−3, respectively) against the COSMOS data in Germany. Notably, the application of the exponential filter significantly improves the performance of the products. Conversely, deseasonalized time series of all selected products demonstrate lower performances across all COSMOS sites. Most products show a considerable positive bias, which limits their usability for the assessment of absolute soil water storage. We also found that the land cover type, mean annual soil moisture, and vertical support have notable influences on the performance of the soil moisture products. Additionally, the performances of the soil moisture products show seasonal variations, such that both correlation and bias are highest during the summer season. This study highlights the strengths of COSMOS data as a robust reference for evaluating soil moisture products. Additionally, it provides insights on how to assess, interpret, and improve large-scale soil moisture products.

Alteration assemblage characterization using machine learning applied to high-resolution drill-core images, hyperspectral data and geochemistry

Integration of multiple data types is beneficial for prediction of geological characteristics. From the perspective that geochemistry characterizes the composition of a rock mass, hyperspectral data characterizes alteration mineralogy and image feature extraction characterizes texture, most geological classifications would be well-informed by the combination of these three features. The process of meaningfully integrating distinctly sourced datasets and producing scale-relevant predictions for geological classifications involves several steps. We demonstrate a workflow to comprehensively structure and integrate these three feature families, refine training data, predict alteration classes and mitigate noise derived from scale mismatch in output predictions. The dataset, compiled from the Josemaria porphyry copper–gold deposit in Argentina, is comprised of more than 14 000 intervals of approximately 2 m, taken from 36 drillholes, where geochemistry was merged with hyperspectral mineralogy represented as tabular pixel abundances, and textural metrics extracted from core imagery, structured into the geochemical interval. Feature engineering and principal component analysis provided insights into the behaviour of the ore system during intermediate steps, as well as providing uncorrelated feature inputs for a random forest predictor. Training data were refined by producing an initial prediction, thresholding the predictions to &gt;70% dominant class probability and using those (high-probability) samples to produce a final model encoding better constrained separation between alteration assemblages. Prediction using the final model returned an accuracy of 82.5%, as a function of model discrepancy combined with logging ambiguity and a scale mismatch between generalized logged intervals and much more granular (2 m) feature inputs. Noise reduction and generalization to the desired resolution of output was achieved by applying the multiscale multivariate continuous wavelet transform tessellation method to class membership probabilities. Ultimately, a large database of logged drill core was homogenized using empirical methodologies. The described workflow is adaptable to distinct scenarios with some modification and is apt for integrating multiple input feature types and using them to systematically define geological classifications in drillhole data.

Shouldering the burden: social-ecological scale mismatches in wetland ecosystem management in Aotearoa New Zealand

ABSTRACT Social-ecological mismatches in scale limit the recovery of ecosystems from environmental degradation, severely impacting the diverse groups who rely on them. Identifying scale mismatches across cultural groups provides insights into underlying social and structural inequities affecting the management, recovery, and use of natural ecosystems. It can also present pathways to remediate mismatches and inequities. Here, we investigated the nature of social-ecological scale mismatches reported by four actor groups (tangata tiaki (Māori environmental guardians), private landowners, Crown (i.e. State) agencies, and recreational gamebird hunters) associated with wetland ecosystems in Aotearoa New Zealand. We also sought to uncover the nature of burdens and benefits brought about by mismatches and whether these were reportedly shared by all groups. Spatial, temporal, and functional-conceptual scale mismatches enabled by Western-based governance (e.g. policies geared towards individual farm-scale rather than catchment-scale wetland management) were reported to undermine aspects of social, cultural, and environmental wellbeing (e.g. through reduced agency). Mismatches such as focus on short-term economic gain over long-term wetland ecosystem health were reported to hamper particularly Indigenous peoples’ rights and responsibilities towards natural ecosystems. Equitable partnerships and nested governance are mechanisms within biocultural frameworks that can support self-determination by Indigenous peoples. Fostering cultural diversity by embracing value plurality and weaving diverse knowledge systems can improve social-ecological outcomes for both Indigenous and other actor groups. Social justice through equitable management and use of resources can then cascade to promote social-ecological wellbeing, thus benefitting both humans and other elements of nature.

Moving away from a scale mismatch: Spatiotemporal modelling of striped shrimp (Pandalus montagui) density in Canada’s subarctic

Mismatches between assessment and ecological processes can increase the likelihood of resource mismanagement, lead to the misinterpretation of population dynamics, and impact decision making. Striped shrimp (Pandalus montagui) are an important part of Canada’s subarctic marine ecosystems but have little historical research or assessment focus. Off northern Canada, striped shrimp are currently assessed separately within three assessment areas, despite these areas forming an open, dynamic system with population mixing throughout. Although assessed using data from a common survey, the differing time series of available data within each area have hampered efforts to consider the dynamics of the stock as one population and create a single biomass index for the area. Consequently, there is a spatial-scale mismatch between the biological resource and the assessment and management scales. To address causes of this scale mismatch, we developed a spatiotemporal model that can accommodate inconsistent survey coverage and can be used to estimate annual biomass of fishable striped shrimp throughout the study area over the entire time series (2005–2022). Meanwhile, to address deficiencies in biological knowledge, we concomitantly explored potential environmental and ecosystem covariates (i.e., bottom temperature, bottom salinity, and predicted potential predator densities) to further scientific understanding of striped shrimp’s relationships with their ecosystem. Our results build a cohesive population-level biomass time series and found fishable striped shrimp densities were related to depth, bottom temperature, and lagged predicted redfish density.

Identification of Shale Lithofacies from FMI Images and ECS Logs Using Machine Learning with GLCM Features

The identification of sedimentary structures in lithofacies is of great significance to the exploration and development of Paleogene shale in the Boxing Sag. However, due to the scale mismatch between the thickness of laminae and the vertical resolution of conventional wireline logs, the conventional lithofacies division method fails to realize the accurate classification of sedimentary structures and cannot meet the needs of reservoir research. Therefore, it is necessary to establish a lithofacies identification method with higher precision from advanced logs. In this paper, a method integrating the gray level co-occurrence matrix (GLCM) and random forest (RF) algorithms is proposed to classify shale lithofacies with different sedimentary structures based on formation micro-imager (FMI) imaging logging and elemental capture spectroscopy (ECS) logging. According to the characteristics of shale laminae on FMI images, GLCM, an image texture extraction tool, is utilized to obtain texture features reflecting sedimentary structures from FMI images. It is proven that GLCM can depict shale sedimentary structures efficiently and accurately, and four texture features (contrast, entropy, energy, and homogeneity) are sensitive to shale sedimentary structures. To accommodate the correlation between the four texture features, the random forest algorithm, which has been proven not to be affected by correlated input features, is selected for supervised lithofacies classification. To enhance the model’s ability to differentiate between argillaceous limestone and calcareous mudstone, the carbonate content and clay content calculated from the ECS logs are involved in the input features. Moreover, grid search cross-validation (CV) is implemented to optimize the hyperparameters of the model. The optimized model achieves favorable performance on training data, validation data, and test data, with average accuracies of 0.84, 0.79, and 0.76, respectively. This study also discusses the application of the classification model in lithofacies and production prediction.

Layout Dependence Stress Investigation in through Glass via Interposer Architecture Using a Submodeling Simulation Technique and a Factorial Design Approach.

The multi-chiplet technique is expected to be a promising solution to achieve high-density system integration with low power consumption and high usage ratio. This technique can be integrated with a glass interposer to accomplish a competitive low fabrication cost compared with the silicon-based interposer architecture. In this study, process-oriented stress simulation is performed by the element activation and deactivation technique in finite element analysis architecture. The submodeling technique is also utilized to mostly conquer the scale mismatch and difficulty in mesh gridding design. It is also used to analyze the thermomechanical responses of glass interposers with chiplet arrangements and capped epoxy molding compounds (EMC) during curing. A three-factor, three-level full factorial design is applied using the analysis of variance method to explore the significance of various structural design parameters for stress generation. Analytic results reveal that the maximum first principal stresses of 130.75 and 17.18 MPa are introduced on the sidewall of Cu-filled via and the bottom of the glass interposer, respectively. Moreover, the EMC thickness and through glass via pitch are the dominant factors in the adopted vehicle. They significantly influence the stress magnitude during heating and cooling.

A multilevel social-ecological network approach for reconciling coastal saltmarsh conservation and development

In a large-scale region, governance for connectivity in an ecological system often conflicts with management boundaries, causing inefficiencies. Collaboration among management organizations in different areas can help overcome this problem. However, few studies quantified the collaborations' practical relationship with connectivity, considering that some potentially connected paths are easy to neglect by managers. In this paper, collaborations among government agencies in project application process were analyzed, and a multilevel social-ecological network analysis (SENA) approach was developed to identify the collaboration's effect on genetically connected coastal areas. The network framework and methods were shown in a case of coastal saltmarsh conservation and development in the Yellow River Delta, China. Collaboration patterns in conservation and development networks were analyzed and compared among local, subregional, and regional government agencies working in genetically connected coastal areas. Project information flow, reflecting communication frequency and decision-making chances among government agencies was quantified and correlated with ecological connectivity to inform governance effects. Results showed areas with the potential to realize social-ecological alignment, where collaborative networks were measured by network density (percentage of connected network edges). The current reveals that development has more significant potential than conservation at most levels to overcome the misalignment of the social-ecological system, also known as scale mismatch. Empirical evidence also showed a correlation between communication capacity in development networks and improved ecological conditions. The multilevel SENA advanced in this paper can be used for natural resource management when connectivity plays a major role.

How can digital finance boost enterprises' high-quality development?: evidence from China.

As a new industry derived from the traditional financial system and enhanced by emerging technologies, digital finance is significant in microenterprise development. Based on the 2011-2018 Digital Inclusive Finance Index of Peking University, we examine the mechanism-inventory optimization and incremental innovation. We use the data of small and medium-sized enterprises to reveal the effect of digital finance-mismatch correction and defect improvement. The study results show that digital financing significantly improves enterprises' high-quality development. Further research has revealed that digital finance can effectively correct scale, attribute, phase, and industry mismatches. Digital finance alleviates financing constraints, solves the expensive financing problem, and pushes enterprises to deleverage in the economic development process. However, digital finance is still limited for companies with high financing constraints and leverage. Moreover, financial regulation can significantly improve the economic performance of digital finance. The findings provide reliable empirical evidence and policy inspiration for promoting digital finance development, deepening the supply-side structural reform of finance, better serving the real economy, and achieving high-quality economic development.

Water resource security assessment and prediction in a changing natural and social environment: Case study of the Yanhe Watershed, China

Water security is the backbone of regional development. However, it is generally compromised by the increasing competition for water resources between natural and social systems, especially in areas with limited water resources. Sustainable water planning requires a comprehensive analysis of supply and demand patterns, which is difficult owing to scale mismatches in data and methodologies. This study investigated the dynamics and interrelationships of water supply and demand in a semi-arid watershed of the Loess Plateau, China, by developing a system dynamics model that simulates water security during 1980–2020 and future changes under different climate, socioeconomic development, and inter-basin water diversion scenarios during 2021–2050. The results showed that (1) by the end of 2020, the regional water security level gradually approached the water scarcity threshold indicated by the water deficit index surpassing 0; (2) under unfavorable climate and progressive socioeconomic growth conditions, up to 16 out of 30 years of simulation demonstrated water insecurity in the form of a net water quantity deficit; and (3) the water diversion project can offset such deficits in the short term, but potentially aggravates water insecurity risk by eliciting a more rapid socioeconomic growth pattern. The results indicate that curbing demand and improving water-saving technology are instructive in water resource management. For the Yanhe Watershed, the quantity and pattern of water diversion should be improved in the future to guarantee water resource security on a full temporal scale. We suggest a moderate socioeconomic growth rate to be more appropriate for this region and that efforts to solidify water use redlines and advancement in water-saving technologies are essential in the future. This study provides a scientific basis for the long-term evaluation of regional water security on a full timescale with tightly coupled hydrological components and a modeling framework for decision-makers to manage water resources sustainably.

Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation

Gross primary productivity (GPP) is a measure of carbon uptake by terrestrial ecosystems for carbon neutrality and serves as a key indicator for the Sustainable Development Goals. The instantaneous Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) is applied in GPP estimation by light use efficiency (LUE) models. However, an obvious time scale mismatch exists between instantaneous FAPAR and daily accumulated GPP. In the study, we explored the potential of temporal upscaling instantaneous FAPAR in improving GPP simulation. The absorbed photosynthetically active radiation (APAR) derived from instantaneous and upscaled FAPARs were first compared. GPPs were estimated from instantaneous and upscaled FAPAR by MOD17 LUE model using default and optimized parameters. The optimized GPP was finally compared with three GPP products: GLASS, MOD17A2H and MYD17A2H GPP. The APARs and GPPs were evaluated with the Eddy Covariance (EC) GPP at 78 forest sites from the Fluxnet community. Results showed that when compared with EC GPP, the upscaled FAPAR-derived APAR held a higher R2 (0.26–0.73) than that from instantaneous FAPARs. The upscaled FAPAR-based GPPs by MOD17 LUE model with default or optimized parameters had a larger R2 and smaller RMSE reducing uncertainties by 6.82–7.8% (maximum value: 27.9–49%). The optimized upscaled FAPAR-derived GPP was far superior to the GPP products with larger R2 (0.69–0.85) and smaller RMSE (12.8–14.6 g C/m2/8d). It is concluded that temporal upscaling of MODIS instantaneous FAPAR can well improve the estimation of forest GPP and the parameters of MOD17 LUE model should be further optimized. Our study is an effort toward reducing the uncertainties from FAPAR in the GPP estimation for better assessing the achievement of carbon neutrality and Sustainable Development Goals.

Inconsistent Coral Bleaching Risk Indicators Between Temperature Data Sources

AbstractCoral reefs are facing severe threats and are at risk of accelerated decline due to climate change‐induced changes in their environment. Ongoing efforts to understand the mechanisms of coral response to warming rely on multiple sources of temperature data. Yet, it remains uncertain whether the Sea Surface Temperature (SST) data used for coral reef studies are consistent among different data products, despite potential implications for conservation. A better understanding of the consistency among the different SST data applied to coral reefs may facilitate the fusion of data into a standard product. This will improve monitoring and understanding of the impact of global warming on coral reefs. Four types of SST data across North‐Western and South‐Western Australia are compared to assess their differences and ability to observe high thermal stress during historical coral bleaching events. The four SST data sources included those derived from Global Circulation Models, NOAA CoralTemp SST product, ESA CCI SST product, and coral core derived SST. Coral bleaching risk indicators, Degree Heating Week (DHW), and Degree Heating Month (DHM) were calculated using these sources and compared for consistency. DHW and DHM were inconsistent among data sets and did not accurately reflect high thermal stress metrics during moderate and severe bleaching events. Some reefs did not experience bleaching in spite of high DHWs and DHMs, suggesting a mismatch in data scales, or perhaps other oceanographic factors and coral adaptation. By exploring the differences and similarities among these four data sources, this study highlights the need to compare existing indicators of thermal stress from different data sets.

Two-Terminal MoS2 Memristor and the Homogeneous Integration with a MoS2 Transistor for Neural Networks.

Memristors are promising candidates for constructing neural networks. However, their dissimilar working mechanism to that of the addressing transistors can result in a scaling mismatch, which may hinder efficient integration. Here, we demonstrate two-terminal MoS2 memristors that work with a charge-based mechanism similar to that in transistors, which enables the homogeneous integration with MoS2 transistors to realize one-transistor-one-memristor addressable cells for assembling programmable networks. The homogenously integrated cells are implemented in a 2 × 2 network array to demonstrate the enabled addressability and programmability. The potential for assembling a scalable network is evaluated in a simulated neural network using obtained realistic device parameters, which achieves over 91% pattern recognition accuracy. This study also reveals a generic mechanism and strategy that can be applied to other semiconducting devices for the engineering and homogeneous integration of memristive systems.

A Dual-Gated Structures for Lossless Ion Manipulations-Ion Mobility Orbitrap Mass Spectrometry Platform for Combined Ultra-High-Resolution Molecular Analysis.

High-resolution ion mobility spectrometry-mass spectrometry (HR-IMS-MS) instruments have enormously advanced the ability to characterize complex biological mixtures. Unfortunately, HR-IMS and HR-MS measurements are typically performed independently due to mismatches in analysis time scales. Here, we overcome this limitation by using a dual-gated ion injection approach to couple an 11 m path length structures for lossless ion manipulations (SLIM) module to a Q-Exactive Plus Orbitrap MS platform. The dual-gate setup was implemented by placing one ion gate before the SLIM module and a second ion gate after the module. The dual-gated ion injection approach allowed the new SLIM-Orbitrap platform to simultaneously perform an 11 m SLIM separation, Orbitrap mass analysis using the highest selectable mass resolution setting (up to 140 k), and high-energy collision-induced dissociation (HCD) in ∼25 min over an m/z range of ∼1500 amu. The SLIM-Orbitrap platform was initially characterized using a mixture of standard phosphazene cations and demonstrated an average SLIM CCS resolving power (RpCCS) of ∼218 and an SLIM peak capacity of ∼156, while simultaneously obtaining high mass resolutions. SLIM-Orbitrap analysis with fragmentation was then performed on mixtures of standard peptides and two reverse peptides (SDGRG1+, GRGDS1+, and RpCCS = 305) to demonstrate the utility of combined HR-IMS-MS/MS measurements for peptide identification. Our new HR-IMS-MS/MS capability was further demonstrated by analyzing a complex lipid mixture and showcasing SLIM separations on isobaric lipids. This new SLIM-Orbitrap platform demonstrates a critical new capability for proteomics and lipidomics applications, and the high-resolution multimodal data obtained using this system establish the foundation for reference-free identification of unknown ion structures.

Improving Vortex Position Accuracy with a New Multiscale Alignment Ensemble Filter

Abstract A multiscale alignment (MSA) ensemble filtering method was introduced by Ying to reduce nonlinear position errors effectively during data assimilation. The MSA method extends the traditional ensemble Kalman filter (EnKF) to update states from large to small scales sequentially, during which it leverages the displacement vectors derived from the large-scale analysis increments to reduce position errors at smaller scales through warping of the model grid. This study stress tests the MSA method in various scenarios using an idealized vortex model. We show that the MSA improves filter performance as number of scales (Ns) increases in the presence of nonlinear position errors. We tuned localization parameters for the cross-scale EnKF updates to find the best performance when assimilating an observation network. To further reduce the scale mismatch between observations and states, a new option called MSA-O is introduced to decompose observations into scale components during assimilation. Cycling DA experiments show that the MSA-O consistently outperforms the traditional EnKF at equal computational cost. A more challenging scenario for the MSA is identified when the large-scale background flow and the small-scale vortex are incoherent in terms of their errors, making the displacement vectors not effective in reducing vortex position errors. Observation availability for the small scales also limits the use of large Ns for the MSA. Potential remedies for these issues are discussed.

Community-based climate adaption: A perspective on the interface between a common pool resource system and an individual-based market transaction system

Because of land privatisation and marketisation in rural areas, community-based adaptation to climate change may face new challenges. A field survey conducted on the Qinghai–Tibetan Plateau (QTP) shows that herders with a grassland collective management system (CMS) suffer higher livestock mortality than those with an individual management system (IMS) during the same extreme climatic events, in contrast to previous research findings. This study seeks to explain this contrast. The results show that although local herders have begun to rely on market-related adaptation strategies to cope with climate change, IMS herders are more inclined to rent-in grassland, while CMS herders are more inclined to purchase fodder. The high-cost grassland renting-in strategy reduces livestock mortality and total household economic loss more effectively than purchasing fodder during snow disasters. An important reason for this is that IMS strengthens market concepts and promotes interaction between herders and external markets, especially the grassland rental market, while CMS continues past grazing traditions and maintains traditional social relationships and collective concepts within the community. CMS herders fail to rent-in grassland due to psychological free-riding incentives and scale mismatch. In the face of repeated climatic disasters, however, CMS herders have also begun to overcome various obstacles to entering the grassland rental market through self-organization and are gradually forming a new pathway of adaptation to climate change.

Suppressing transverse mode instability through multimode excitation in a fiber amplifier

High-power fiber laser amplifiers have enabled an increasing range of applications in industry, science, and defense. The power scaling for fiber amplifiers is currently limited by transverse mode instability. Most techniques for suppressing the instability are based on single- or few-mode fibers in order to output a clean collimated beam. Here, we study theoretically using a highly multimode fiber amplifier with many-mode excitation for efficient suppression of thermo-optical nonlinearity and instability. We find that the mismatch of characteristic length scales between temperature and optical intensity variations across the fiber generically leads to weaker thermo-optical coupling between fiber modes. Consequently, the transverse mode instability (TMI) threshold power increases linearly with the number of equally excited modes. When the frequency bandwidth of a coherent seed laser is narrower than the spectral correlation width of the multimode fiber, the amplified light maintains high spatial coherence and can be transformed to any target pattern or focused to a diffraction-limited spot by a spatial mask at either the input or output end of the amplifier. Our method simultaneously achieves high average power, narrow spectral width, and good beam quality, which are required for fiber amplifiers in various applications.

Supporting the planning of urban blue-green infrastructure for biodiversity: A multi-scale prioritisation framework

Primary considerations for urban blue-green infrastructure (BGI) encompass sustainable stormwater/urban heat management while biodiversity conservation is often considered an inherent benefit rather than a core planning requirement. However, ecological function of BGI as ‘stepping stones’ or linear corridors for otherwise fragmented habitats is undisputed. While quantitative approaches for modelling ecological connectivity in conservation planning are well established, mismatches in scope and scale with models that support the planning of BGI makes their adoption and integration difficult across disciplines. Technical complexities have led to ambiguity around circuit and network-based approaches, focal node placement, spatial extents, and resolution. Furthermore, these approaches are often computationally intensive, and considerable gaps remain in their use for identifying local-scale critical “pinch-points” that urban planners may respond to with the integration of BGI interventions that address biodiversity enhancement among other ecosystem services. Here, we present a framework that simplifies and integrates the merits of regional connectivity assessments with a focus on urban areas to prioritise BGI planning interventions while reducing computational demands. Our framework facilitates: (1) modelling potential ecological corridors at a coarse regional scale, (2) prioritising local-scale BGI interventions based on the relative contribution of individual nodes in this regional network, and (3) inferring connectivity hot- and cold-spots for local-scale BGI interventions. We illustrate this in the Swiss lowlands, demonstrating how, compared to previous work, we are able to identify and rank different priority locations across the region for BGI interventions in support of biodiversity enhancement and how their local-scale functional design may be benefited by addressing specific environmental variables.