Scale Heterogeneity Research Articles

Assessing field scale spatiotemporal heterogeneity in salinity dynamics using aerial data assimilation

Soil moisture and salinity shifts within the root zone can significantly alter crop yields. Thus, spatiotemporal dynamics of these parameters are essential for precision crop management. Airborne or spaceborne earth observation methods based on vegetation and soil observations have sometimes been used with limited success to indirectly understand parameters like soil salinity. These datasets lack spatiotemporal resolution to discern field-scale heterogeneities, and estimates’ accuracy is poor. A Metropolis-Hasting Markov Chain-Monte Carlo (MCMC) based method was developed to estimate field-scale soil salinity by assimilating estimated evapotranspiration (ET) data obtained from aerial canopy temperature sensing with ET outputs from a one-dimensional soil-water transport model. By aligning the two estimated ET values, we inferred anticipated soil salinity levels in a mature pecan orchard (28,951 m2). Our results aligned closely with in-situ measurements with a spatial cross-correlation more than 0.86 and highlighted the expected heterogeneities and nonlinearities. This research offers an approach to refine the current state-of-the-art crop models by accounting for field scale heterogeneities using remotely sensed data. This assimilation method will pave the way for a more inclusive agricultural system modeling that can infer critical but hard-to-measure soil properties from easier-to-obtain remotely sensed datasets. Though this paper concentrates on aerial observations, we anticipate similar methods can be used for satellite-based imagery, especially those with high spatial, temporal, and spectral resolutions.

ML Approaches for the Study of Significant Heritage Contexts: An Application on Coastal Landscapes in Sardinia

Remote Sensing (RS) and Geographic Information Science (GIS) techniques are powerful tools for spatial data collection, analysis, management, and digitization within cultural heritage frameworks. Despite their capabilities, challenges remain in automating data semantic classification for conservation purposes. To address this, leveraging airborne Light Detection And Ranging (LiDAR) point clouds, complex spatial analyses, and automated data structuring is crucial for supporting heritage preservation and knowledge processes. In this context, the present contribution investigates the latest Artificial Intelligence (AI) technologies for automating existing LiDAR data structuring, focusing on the case study of Sardinia coastlines. Moreover, the study preliminary addresses automation challenges in the perspective of historical defensive landscapes mapping. Since historical defensive architectures and landscapes are characterized by several challenging complexities—including their association with dark periods in recent history and chronological stratification—their digitization and preservation are highly multidisciplinary issues. This research aims to improve data structuring automation in these large heritage contexts with a multiscale approach by applying Machine Learning (ML) techniques to low-scale 3D Airborne Laser Scanning (ALS) point clouds. The study thus develops a predictive Deep Learning Model (DLM) for the semantic segmentation of sparse point clouds (<10 pts/m2), adaptable to large landscape heritage contexts and heterogeneous data scales. Additionally, a preliminary investigation into object-detection methods has been conducted to map specific fortification artifacts efficiently.

Soil organic carbon sequestration can be promoted through the improvement of landscape configuration heterogeneity in typical agricultural regions of northeast China

Landscape heterogeneity is considered a promising option for building resilient and sustainable agroecosystems. Understanding the relationships between farmland soil organic carbon (SOC) and landscape heterogeneity can support soil carbon sequestration and better serve food security and climate change. However, the influence extent and optimal scale of farmland landscape heterogeneity (i.e. landscape composition and landscape configuration heterogeneity) on SOC remains unclear. In this study, we established the relationships between multi-scale landscape heterogeneity and SOC in a typical grain-production county of northeast China. Stepwise regression results showed that when the buffer radius was 2000 m, the interpretation of SOC by landscape heterogeneity was the largest. The effects of landscape composition and landscape configuration on SOC were further decomposed by Variance Partitioning Analysis, and we found that independent interpretation ability of landscape configuration (8%) exceeded landscape composition (7%). The result of soil mapping combined with landscape indexes also showed that landscape configuration contributed more to the increased accuracy. Moreover, we found that correlation between configuration indexes and SOC at the class level was less related than that at the landscape level, among which the two most important indexes were Mean Fractal Dimension Index (FRAC_MN) and Number of Patches (NP). FRAC_MN was even more important than natural factors, indicating the validity of landscape as an indicator of human activities should not be ignored when considering farmland SOC. Overall, the results of this study revealed that the negative effects of agricultural intensification on SOC can be buffered to a certain extent by increasing the complexity of patch shape and reducing the degree of landscape fragmentation at the landscape level, providing hope for the sustainable development in intensive agricultural areas. In addition, due to the scale effect of landscape heterogeneity on farmland SOC, we suggest that decision makers should consider the spatial scale in landscape allocation and planning. This study provides a scientific reference for realizing the balance between grain production and ecological function in intensive agricultural areas.

Assessing the Performance of Flux Imbalance Prediction Models Using Large Eddy Simulations Over Heterogeneous Land Surfaces

Accurate representation of heat fluxes is crucial for understanding land–atmosphere interactions and improving atmospheric simulations. However, a common issue arises with flux imbalance, where the measured turbulent heat flux tends to be underestimated due to the nonlocal effects of atmospheric secondary circulations. This study evaluated four flux imbalance prediction models by analyzing data from large eddy simulations performed over heterogeneous land surfaces. For that, a checkerboard pattern of soil moisture was used to define the lower boundary conditions for the atmosphere, across heterogeneity scales ranging from 50 m to 2.4 km. The results show that the selected models can effectively predict flux imbalance when provided with proper semi-empirical factors. The presence of two distinct secondary circulations, thermally-induced mesoscale circulation and turbulent organized structures, account for the nonlinear effect of the heterogeneity scale on the flux imbalance, but it does not affect the performance of the selected models. This study suggests that the flux imbalance prediction models are useful for improving e.g. eddy-covariance measurements. Additionally, a quadrant analysis showed an increasing difference between ejections and sweeps with height, which explains the decrease and increase of the turbulent heat flux and flux imbalance, respectively, and underscores the importance of accounting for vertical variations in turbulent fluxes to represent atmospheric processes accurately.

Insights from tensile fracture properties and full-field strain evolution of deep coral reef limestone under dynamic loads

Coral reef limestone (CRL) commonly undergoes dynamic tension when underground structures of island reefs encounter impacts, explosions, or seismic activities. Given the complexity of biological pores, the dynamic tensile fracture characteristics of CRL are poorly understood. Therefore, the dynamic tensile fracture behaviors of deep CRL were systematically observed by Split Hopkinson Pressure Bar tests and digital image techniques. Comparing to traditional rocks, the macro-pores near failure band would significantly change cracking path. The failure patterns are dominated by loading rate. The strongly dependence of dynamic tensile strength and dynamic crack initiation toughness on loading rate suggests the two indices overcome the effect of CRL macro-pores under dynamic impacts. At low loading rates, tensile fractures predominantly follow intergranular cracks, whereas transgranular cracks dominate at higher rates. The fractal dimension of fracture surface decreases with increasing crack propagation velocity, loading rate, and dynamic crack initiation toughness. Due to the unique marine sedimentary environment, the mechanical heterogeneity in multiple scales distinguishes CRL from terrestrial rock materials. The insights into underlying mechanisms of dynamic tension provide support to optimization of blasting scheme and stability assessments for island underground engineering.

Influence of zwitterionic amphiphilic copolymers on heterogeneous gypsum formation: A promising approach for scaling resistance

This study aims to investigate the influence of zwitterionic amphiphilic copolymers (ZACs) in the nucleation and growth of heterogeneous CaSO4 at the zwitterion-water interface, which is crucial for the prevention of mineral scaling and consequent downtime or suboptimal performance in industries like membrane desalination, heat exchangers, and pipeline transportation. In situ grazing incidence small angle X-ray Scattering (GISAXS), and quartz crystal microbalance with dissipation (QCM-D) techniques were used to analyze the evolution of CaSO4 particles on two new ZAC coatings: poly-(trifluoroethyl methacrylate-random-sulfobetaine methacrylate) (PTFEMA-r-SBMA, or PT:SBMA) and poly(trifluoroethyl methacrylate-random-2-methacryloyloxyethyl phosphorylcholine) (PTFEMA-r-MPC, or PT:MPC). The results showed that PT:MPC coatings promoted nucleation but inhibited crystal growth, resulting in slower overall reaction kinetics on PT:MPC coatings compared to PT:SBMA coatings. Interfacial interactions involving the substrates, sulfate minerals, and ions were examined, revealing that calcium ion adsorption, primarily governed by electrostatic attraction, played a crucial role in the nucleation and growth processes on both ZAC coatings. The crystal characterization revealed a phase transition from bassanite to gypsum on both ZAC coatings, suggesting that these zwitterionic materials can influence the mineral phase of heterogeneously formed CaSO4 crystals. These findings enhance our understanding of the fundamental mechanisms underlying heterogeneous CaSO4 scaling in the presence of zwitterionic materials.

Mechanisms of electromagnetic field control on mineral scaling in brackish water reverse osmosis: Combined homogenous and heterogeneous nucleation

Electromagnetic field (EMF) treatment has emerged as a promising approach for scaling control due to its cost-effectiveness, simplicity, and low energy consumption. However, there is a limited understanding of the mechanisms by which applied EMF impacts mineral scaling in reverse osmosis (RO) systems. This has led to inconclusive and varied results and uncertainties regarding its effectiveness. This study elucidates the impacts of EMF on homogenous and heterogeneous nucleation and membrane performance during RO desalination of different feedwaters. Our results reveal that EMF exhibits greater efficacy in treating near-saturated water (SI∼0), especially when coupled with extended hydraulic flushing (HF). For saturated brackish water desalination, heterogeneous scaling predominantly occurs on membrane surfaces, with the effectiveness of EMF in inhibiting scaling primarily attributed to the hydration effect. In supersaturated solutions, EMF promotes bulk precipitation due to the magnetohydrodynamic effect, quickly blocking membrane pores. Thus, when the saturation reaches a certain high level during RO desalination, magnetohydrodynamic EMF effects can accelerate flux decline caused by homogeneous scaling. This work provides an efficient method for predicting EMF efficiency, emphasizing the importance of saturation conditions and HF cleaning duration in determining membrane performance, suggesting these show promise for improving undersaturated or near-saturated feedwater desalination via RO.

In-line granular filtration for mitigating gypsum scaling in membrane distillation

Mineral scaling and scaling-induced wetting are major challenges for the widespread application of membrane distillation (MD) in saline wastewater treatment. For the first time, an in-line modified activated alumina (AA) granular filtration coupled with MD process was proposed to achieve effective control of gypsum scaling. Firstly, the AA modified by NaOH impregnation with tuned pore structure and surface characteristics were supplied as the filter media. The optimized MD performance was obtained using in-line filtration with modified AA (MAA) with 20 g/L NaOH and the ratio of AA and NaOH solution of 1:3 for the duration of 24 h. Compared with severe scaling without granular filter, the optimized performance with a final normalized flux higher than 0.7 and a permeate conductivity less than 5 μS/cm at 70 % water recovery was observed. The MAA with rich pore, scale-like structures and high Na loading showed a superior ability in capturing gypsum scaling due to the enhanced heterogeneous scaling and the retention of scaling. In-line granular filtration-MD was also effective in mitigating gypsum scaling and scaling-induced wetting in saline solutions (e.g., 7–12 g/L NaCl). Our findings provide guidance for surface design of granular filter media and scaling control in membrane desalination technology.

Trade-Off and Synergy Relationships and Driving Factor Analysis of Ecosystem Services in the Hexi Region

The Hexi region, located in a sensitive and fragile ecological zone in northwest China, requires a scientific assessment of ecosystem services and their interactions. Identifying the main factors influencing spatial distribution is crucial for the sustainable development and effective management of the region. This study evaluates key ecosystem services, including regulating services (water conservation, soil conservation, carbon storage) and provisioning services (NPP), using Spearman’s correlation and pixel-by-pixel spatial analysis to calculate spatial trade-offs and synergies. Geographic detectors were used to uncover the underlying driving mechanisms. The results show that: (1) From 2000 to 2020, soil conservation, NPP, and carbon storage showed fluctuating growth, while water conservation declined. Spatially, high-value areas of water conservation, carbon storage, and NPP were concentrated in the central and southern areas, while high values of soil conservation services were mainly in the northwest and southeast regions. (2) The trade-offs and synergies among various ecosystem services exhibit temporal shifts, along with spatial scale effects and heterogeneity. In the study area, the proportion of pixels showing a trade-off relationship between water conservation and soil conservation, and between water conservation and NPP, accounts for 48.21% and 21.42%, respectively. These trade-offs are mainly concentrated in the central and southeastern regions, while the northwestern counties predominantly exhibit synergies. (3) Precipitation was the dominant factor for water conservation, carbon storage, and NPP, as well as for the trade-offs among these services. Among natural factors, climatic factors were significantly more influential than socio-economic factors, and the interaction between two factors had a greater explanatory power than single factors.

Linking petrophysical heterogeneity and reservoir rock-typing of the post-rift shallow marine siliciclastics to their depositional setting: The Upper Cretaceous Bahariya reservoirs, north Western Desert, Egypt

The upper Cretaceous clastic facies of the Bahariya Formation host the main reservoir intervals in the north Western Desert (NWD) of Egypt. These clastics were deposited in a parallic depositional environment characterized by different scales of reservoir heterogeneities. However, the link between these heterogeneities and the depositional setting of the reservoir facies is still blurred. In this study, we investigate a highly-heterogeneous reservoir facies of Bahariya Formation in Yasser Field wells, NWD in order to construct a predictive framework for the distribution of the best reservoir rock types (RRTs) and flow zones. Seismic stratigraphy was integrated with core sedimentology to understand the depositional architecture of the reservoir facies. Moreover, conventional core analysis and wireline logs were interpreted to evaluate the scales of lithological and petrophysical heterogeneities in the different RRTs.In the studied Yasser Field, the Bahariya Formation consists of tidal facies deposited in a wide range of tidally-influenced conditions. The seismic facies varies greatly with variation in the tidal regime. Best reservoir rock types RRTs are associated with tidal channels and amalgamated tidal bars (RRTI). RRTI rocks represent the main fluid flow conduits in the studied Bahariya reservoir. Lithological and petrophysical heterogeneities are more prominent in the tidal mud flat and mixed tidal flat facies which form RRTIII rocks. Linking seismic with sedimentary facies enabled us to predict the distribution of the best reservoir flow zones in the study region. The present results establish a framework for predicting the optimum reservoir quality facies based on integrating seismic, sedimentary and petrofacies. This framework could be applied in analogous tidally-influenced reservoir facies with high depositionally-controlled pore system heterogeneity.

Fabrication of highly heterogeneous precipitate microstructure in an α/β titanium alloy

To obtain a synergistic combination of high strength and high ductility in titanium alloys, design and creation of heterogenous precipitate microstructure have attracted increasing attention. Herein, using Ti-3Al-5Mo-4.5V (wt.%, an α/β titanium alloy) as a model alloy, we demonstrated that a highly heterogenous α-phase precipitate microstructures with well-controlled length scale of spatial heterogeneity (i.e., micron-sized primary α and nano-scale secondary α precipitates), can be synthesized through activating the ω-assisted α nucleation transformation pathway that operates in metastable β titanium alloy alone. A detailed analysis of transformation pathway for ω phase and the underlying ω-assisted α nucleation mechanisms is carried out using the integrated advanced characterizations, theoretical calculations and simulations based on DFT, and phase-field modeling. Experimental results show unambiguously that the embryonic ω particles (also known as athermal ω) with partially collapsed structure are incapable of refining secondary α (αs). In contrast, the isothermal ω particles with a complete structural collapse play an important role in assisting αs nucleation, resulting in the formation of the ultrafine αs lamellae at nanoscales after two-step aging heat treatments. The contributions from the isothermal ω particles are then quantified using first-principles calculations and phase-field simulations. It is found that, even though the solute partitioning between a growing isothermal ω particle reduces the αs nucleation driving force in the surrounding β matrix, the elastic interaction between the ω particles and the αs nucleus provides more driving force for αs nucleation at specific locations of the ω/β interface. Our work extends the microstructure design strategy based on ω-assisted α nucleation mechanism from metastable β to α/β Ti-alloys, thereby significantly widening the application space of the strategy.

Modeling the effect of material heterogeneity on the thermo-mechanical behavior of concrete using mesoscale and stochastic field approaches

The adverse impact of high temperatures on concrete is a well-recognized issue that can lead to significant mechanical deterioration and structural integrity loss. Factors such as aggregate type, cement composition, temperature, duration of exposure, and moisture content can substantially influence the fire resistance of concrete. To simulate and better understand the effects arising from the heterogeneity of concrete in a fire situation, a mesoscale approach is proposed, using the Mesh Fragmentation Technique (MFT) to assess the complex thermo-mechanical behavior of concrete. The MFT introduces high aspect ratio interface elements to model crack propagation and interfacial transition zones by means of an appropriated tensile damage constitutive law. In this extended framework, a fully-coupled thermo-mechanical model is proposed. The modeling approach includes considerations of both macroscopic and mesoscopic scales, in which the coarse aggregate, mortar matrix and interfacial transition zones are represented. Besides, a stochastic distribution is assumed for the material properties to account for the lower scale heterogeneity. The main novelty proposed in this study consists in the synergy of mesoscale and stochastic approaches that are herein combined to model the effect of heterogeneity on the concurrent macro-mesoscale thermo-mechanical behavior of concrete. To validate the numerical model’s capabilities in capturing thermally induced cracks, benchmark cases and a simulation of a bending beam exposed to elevated temperatures are presented. The results demonstrate the potential of the proposed approach in predicting the behavior of concrete subjected to thermal loading and the role played by heterogeneity in the thermally induced cracking.

Research on Urban Resilience from the Perspective of Land Intensive Use: Indicator Measurement, Impact and Policy Implications

Land intensive use reflects the spatial structure, agglomeration characteristics, and internal mechanisms of urban economic, social, and ecological system development, significantly impacting urban resilience. Based on panel data from 287 cities in China from 2010 to 2020, this paper measures the levels of land intensive use and urban resilience, and empirically examines the impact mechanism of land intensive use on urban resilience through baseline regression and panel quantile regression. The results reveal that: (1) During the study period, China’s urban land intensive use level has significantly improved. The land intensive use level shows a trend of “the strong become stronger, and the weak are always weak” and “high in the east and low in the west” spatial differentiation, while the urban resilience level showed a trend of accelerated “catching up” of low-resilience cities towards high-resilience cities and “high in the east and low in the west” spatial differentiation as well. (2) Land intensive use significantly promotes effect on urban resilience, and the effect depends on different conditions. (3) Among all dimensions of land intensive use, both land input intensity and land use benefits significantly promote urban resilience, while land use intensity shows an insignificant effect. (4) The impact of land intensive use on urban resilience demonstrates significant scale heterogeneity and geographic regional heterogeneity. Based on these findings, the paper proposes relevant policy suggestions for land intensive use aimed at improving urban resilience, offering guidance for promoting high-quality land use and sustainable urban resilience development.

Microbial life in preferential flow paths in subsurface clayey till revealed by metataxonomy and metagenomics

BackgroundSubsurface microorganisms contribute to important ecosystem services, yet little is known about how the composition of these communities is affected by small scale heterogeneity such as in preferential flow paths including biopores and fractures. This study aimed to provide a more complete characterization of microbial communities from preferential flow paths and matrix sediments of a clayey till to a depth of 400 cm by using 16S rRNA gene and fungal ITS2 amplicon sequencing of environmental DNA. Moreover, shotgun metagenomics was applied to samples from fractures located 150 cm below ground surface (bgs) to investigate the bacterial genomic adaptations resulting from fluctuating exposure to nutrients, oxygen and water.ResultsThe microbial communities changed significantly with depth. In addition, the bacterial/archaeal communities in preferential flow paths were significantly different from those in the adjacent matrix sediments, which was not the case for fungal communities. Preferential flow paths contained higher abundances of 16S rRNA and ITS gene copies than the corresponding matrix sediments and more aerobic bacterial taxa than adjacent matrix sediments at 75 and 150 cm bgs. These findings were linked to higher organic carbon and the connectivity of the flow paths to the topsoil as demonstrated by previous dye tracer experiments. Moreover, bacteria, which were differentially more abundant in the fractures than in the matrix sediment at 150 cm bgs, had higher abundances of carbohydrate active enzymes, and a greater potential for mixotrophic growth.ConclusionsOur results demonstrate that the preferential flow paths in the subsurface are unique niches that are closely connected to water flow and the fluctuating ground water table. Although no difference in fungal communities were observed between these two niches, hydraulically active flow paths contained a significantly higher abundance in fungal, archaeal and bacterial taxa. Metagenomic analysis suggests that bacteria in tectonic fractures have the genetic potential to respond to fluctuating oxygen levels and can degrade organic carbon, which should result in their increased participation in subsurface carbon cycling. This increased microbial abundance and activity needs to be considered in future research and modelling efforts of the soil subsurface.

Bi-Directional and Time-Lagged Associations between Engagement and Mental Health Symptoms in a Group Mindfulness-Based Mental Health Intervention.

There is a high need for accessible avenues for improving mental health among emerging adults, particularly on college campuses. Mindfulness-based intervention (MBI) is a promising avenue for reducing mental health symptoms, but initial discomforts associated with MBI may cause symptoms to fluctuate before decreasing, which presents a barrier to engagement with mindfulness on a daily basis. Consistent mindfulness practice is key for forming habits related to MBI, and engagement with mindfulness at home, including between intervention sessions, is an important predictor of mental health outcomes. Research suggests that mental health symptoms may serve as barriers to their own treatment. Thus, it is important to understand how mental health symptom levels impact adherence to treatment protocols. To improve understanding of symptom-specific barriers to treatment and engagement with mindfulness, the present study collected daily diary surveys about engagement with mindfulness and mental health symptoms from a sample of 62 adults recruited to participate in a six-week mindfulness intervention. We explored mental health symptoms as a predictor of engagement with MBI at the mean level and whether within-person variability in symptoms predicted same-day or time-lagged changes in engagement via mixed-effects associations. Using heterogeneous location scale models, we further explored whether erraticism in either mental health symptoms or engagement with mindfulness predicted the other and if outcomes of the mindfulness intervention were homogeneous among subjects. Results showed that bi-directional and time-lagged associations exist between symptoms and engagement, indicating that there is a nuanced temporal and reciprocal relationship between engagement with mindfulness and mental health symptoms. Daily within-person elevations in engagement with mindfulness were associated with concurrent improvements in mental health but prospective increases in mental health symptoms. We also found that higher engagement (over personal averages) was not consistently associated with improvements in mental health across the sample but was instead associated with greater heterogeneity in outcomes. We also found that increases in mental health symptoms (over personal averages), as well as higher average levels of mental health symptoms, were both associated with lower levels of engagement in the mindfulness treatment protocol.

Materials data science using CRADLE: A distributed, data-centric approach

There is a paradigm shift towards data-centric AI, where model efficacy relies on quality, unified data. The common research analytics and data lifecycle environment (CRADLE™) is an infrastructure and framework that supports a data-centric paradigm and materials data science at scale through heterogeneous data management, elastic scaling, and accessible interfaces. We demonstrate CRADLE’s capabilities through five materials science studies: phase identification in X-ray diffraction, defect segmentation in X-ray computed tomography, polymer crystallization analysis in atomic force microscopy, feature extraction from additive manufacturing, and geospatial data fusion. CRADLE catalyzes scalable, reproducible insights to transform how data is captured, stored, and analyzed.Graphical abstract

Can green credit policy with dual-carbon targets make highly polluting enterprises “green”: A micro-analysis of total factor productivity growth

Reducing carbon emissions from highly polluting enterprises is crucial to meeting the world's overall carbon emission reduction targets. Green credit policy can be effective in guiding enterprises to reduce their carbon emissions and is essential to achieving the dual-carbon targets. This study uses micro-data from a 2017–2022 follow-up survey of industrial enterprises in China and a quasi-natural experiment to evaluate whether green credit policy aligned with the dual-carbon targets enable highly polluting enterprises to become “green” by reducing emissions. The results show that green credit policy can lead highly polluting enterprises to significantly reduce carbon emissions, and total factor productivity (TFP) growth plays an intermediary role in this transition. The different impact of green credit policy on TFP may impede the greening process for highly polluting enterprises, with this hindering effect exhibiting scale heterogeneity. This study offers empirical evidence for evaluating green credit policy aligned with China's dual-carbon target and provide insights into leveraging green credit policy to advance this process.

Ultrasonic wave diffusion-based investigations on microstructural development in multi-scale cementitious materials

Microstructural study of cement and its composites has immense significance from both fundamental and applied research points of view. Concrete, a mesoscale cementitious material, possesses high heterogeneity and inherent complex behavior. The multi-phases of concrete show an evolving nature at any instance of time, starting from the initiation of hydration to the complete maturity level. The hydration products and the aggregate-matrix Interfacial Transition Zone (ITZ) play a crucial role in defining the microstructural arrangement, which determines the mechanical and durability properties. Ultrasonic diffusion-based study has a unique advantage for characterising microstructural behavior in heterogeneous materials as it can decouple the different attenuating parameters- diffusivity (D) and dissipation (σ), where, D reflects the material microstructure and σ indicates the viscoelastic property of the base material. For that purpose, an ultrasonic diffusion study has been carried out in the present research for hydration monitoring in two scales, cement (micro-scale), and concrete (meso-scale) under different transmission modes. Ultrasonic measurements have been processed in the time-frequency domain to deduce the diffusivity and dissipation parameters using Spectral Energy Density distribution over the entire time window. The present research reveals the key findings as follows: (1) the influence of microstructure in ultrasonic wave propagation can be explicitly demonstrated using diffusion based analytical formulation and experimental results; (2) ultrasonic parameters (in terms of diffusivity and dissipation) can provide physical information on microstructural development in cementitious material during its hydration stage; and (3) the diffusion based method can distinguish the complex microstructural behavior across the scales of heterogeneity- micro-scale (cement paste) to meso-scale (concrete).

A dual-scale fused hypergraph convolution-based hyperedge prediction model for predicting missing reactions in genome-scale metabolic networks.

Genome-scale metabolic models (GEMs) are powerful tools for predicting cellular metabolic and physiological states. However, there are still missing reactions in GEMs due to incomplete knowledge. Recent gaps filling methods suggest directly predicting missing responses without relying on phenotypic data. However, they do not differentiate between substrates and products when constructing the prediction models, which affects the predictive performance of the models. In this paper, we propose a hyperedge prediction model that distinguishes substrates and products based on dual-scale fused hypergraph convolution, DSHCNet, for inferring the missing reactions to effectively fill gaps in the GEM. First, we model each hyperedge as a heterogeneous complete graph and then decompose it into three subgraphs at both homogeneous and heterogeneous scales. Then we design two graph convolution-based models to, respectively, extract features of the vertices in two scales, which are then fused via the attention mechanism. Finally, the features of all vertices are further pooled to generate the representative feature of the hyperedge. The strategy of graph decomposition in DSHCNet enables the vertices to engage in message passing independently at both scales, thereby enhancing the capability of information propagation and making the obtained product and substrate features more distinguishable. The experimental results show that the average recovery rate of missing reactions obtained by DSHCNet is at least 11.7% higher than that of the state-of-the-art methods, and that the gap-filled GEMs based on our DSHCNet model achieve the best prediction performance, demonstrating the superiority of our method.

An application of complex networks on predicting the behavior of infectious disease on campus

Networks are widely used in understanding the spread of infectious disease. Universities and colleges are characterized by frequent social gatherings and extensive interpersonal communication, making them ideal applications of complex networks. In this paper, a compound model consisting of a homogeneous (small world) network and a heterogeneous (scale free) network is established, the spreading characteristics of epidemics are analyzed by using the mean‐field (MF) and the heterogeneous mean‐field (HMF) approaches, and the effects of various factors such as the total node number (N), the randomly reconnection probability (p), the initial proportion of infected node (p0), the average degree (k), and the shared nodes (s) are simulated by numerical calculations. The simulation results are in consistent with theoretical predictions, indicating that the randomness effect decreases with increasing k and N and is small when k (k = 8) and N (N = 200) are relatively large; the randomness effect is more significant in the scale free (SF) network than in the small world (SW) network, and the disease spread faster in the SF network; shared nodes can weakly increase the disease spread, but when the shared nodes are involved in an infected and an uninfected network, it can induce the disease outbreak in the uninfected network.