Scale Heterogeneity Research Articles

The double-edged role of the digital economy in firm green innovation: micro-evidence from Chinese manufacturing industry.

The digital economy, which gradually emerged with a new generation of information technologies, has become an unavoidable reality for manufacturing firms in conducting green innovation activities. In this context, using matched panel data at the province and manufacturing firm levels in China during the period 2011-2019 as the sample, this article examines the nonlinear impact of the digital economy on firm green innovation, and further identifies the moderation mechanism of government quality and the heterogeneity of its effects. The two-way fixed-effects model reveals that there is not a simple linear association between the digital economy and firm green innovation as traditionally perceived, but rather an inverted U-shaped relationship that first promotes and then inhibits, which remains robust after applying endogenous and robustness tests. And most provinces have not yet crossed the inflection point; thus, the digital economy overall positively impacts green innovation. Further analysis shows that government quality positively moderates the relationship between the digital economy and firm green innovation, statistically reflecting that the turning point shifts upwards to the right under a higher-quality government. It is worth noting that, when heterogeneity in firm ownership, scale, and region is considered, the inverted U-shaped curve still exists, but the level of the digital economy at the inflection point differs, and the digital economy plays a greater role in promoting green innovation for state-owned, large-scale, or midwestern firms. This research has significant policy implications as it establishes an inverse U-shaped relationship between the digital economy and firm green innovation and indicates that while a firm's green patent output increases with the development of digitalization, it begins to decrease after a limit.

Identifying the spatial heterogeneity in the effects of the construction land scale on carbon emissions: Case study of the Yangtze River Economic Belt, China

Low-carbon emissions are a major research focus to solve the problem of global warming and an important area that China needs to focus on to achieve high-quality development. Construction land scale is a non-negligible factor affecting carbon emissions. However, carbon emission impacts of county-scale spatial heterogeneity in construction land scale are under addressed in contemporary research. To address this gap, this paper took 1042 counties in China's Yangtze River Economic Belt (YREB) and developed datasets of the influencing factors including the construction land scale, GDP, secondary industry output proportion in GDP, residential population, and fixed asset investment. After comparing the ordinary least squares and geographically weighted regression (GWR) models, we applied GWR for more in-depth analyses. The global regression model results showed that the effect of the scale of construction land on carbon emissions was exceedingly significant and that the directions of the impacts coincided with the predictions. Further, local regression model results showed that construction land scale had significant spatial heterogeneity in the impact on carbon emissions and most counties (69.58%) showed significant positive correlations. The counties with significant construction land scale impacts on carbon emissions were concentrated and contiguous in spatial distribution and spatially clustered areas varied, with the clearest impact in the downstream region. The findings help to further identify the spatial heterogeneity of construction land scale impacts on carbon emissions, which provides evidence-based and theoretical support for policymakers to develop spatially differentiated emission reduction measures.

Identifying Multiple Scales of Spatial Heterogeneity in Housing Prices Based on Eigenvector Spatial Filtering Approaches

Interest in studying the urban real estate market, especially in investigating the relationship between house prices and related housing characteristics, is rapidly growing. However, this increasing attention is handicapped by a limited consideration of the multi-scale spatial heterogeneity in these relationships. This study uses the rental price data of 72,466 apartments in the Tokyo metropolitan area to examine spatial heterogeneity in the real estate market at multiple spatial scales. Within the framework of spatially varying coefficient (SVC) modeling, we utilized a random effect eigenvector spatial filtering-based SVC (RE-ESF-SVC) model, an approach not previously employed in real estate studies, and compared it with the traditional ESF-SVC model, which has no random effects. Our results show that: (1) except for one housing characteristic that impacts prices consistently throughout the Tokyo metropolitan area, relationships between other characteristics and prices vary from local to global spatial scales; (2) because of the utilization of random effects, RE-ESF-SVC has the unique advantage of making estimations flexibly while maintaining a high performance.

A perspective of scale differences for studying the green total factor productivity of Chinese laying hens

In people’s daily life, egg is one of the main animal protein foods, which will produce different emissions on its breeding procedure. Therefore, in order to promote the development of China’s layer industry, it is necessary to reduce pollutant emissions by improving efficiency. This paper uses Minimum distance to weak efficient frontier-Metafrontier Malmquist Luenberger (MinDW-MML) index model, by considering environmental factors and scale heterogeneity, to explore the evolution characteristics of laying hens breeding green total factor productivity (LHG) in China based on the data of 24 major laying provinces (municipalities) from 2004 to 2018. The results show that: (1) From 2004 to 2018, medium-scale LHG in China is the highest, the small-scale is the second, and the large-scale is the lowest. In the light of regional distribution, the western region is the highest, followed by the central region, and the eastern region is the lowest. (2) From 2004 to 2018, the overall China’s LHG showed a positive growth, and the decomposition indicators were characterized by decreased efficiency and technological progress. In general, the layer industry is vulnerable and easily affected by external factors. (3) Results from common frontiers and group frontiers exist some differences. The LHG under the common frontier is lower than the LHG under the group frontier. Finally, according to the above empirical results, this paper puts forward policy suggestions to improve LHG and environmental protection suggestions for laying hens.

Scale, origin, and predictability of reservoir heterogeneities in shallow-marine carbonate sequences: A case from Cretaceous of Zagros, Iran

This study presents an integrated reservoir heterogeneity analysis of an Iranian carbonate oil reservoir (Sarvak Formation) in the Zagros region. Both static and dynamic aspects of reservoir heterogeneities are considered by integration of routine–special core data and geological attributes (i.e., depositional facies and textures, diagenetic alterations, pore types, and sequence stratigraphic positions). Core porosity and permeability, mercury injection capillary pressure, and scanning electron microscopy data are used along with the results of petrographic studies to define petrographic rock types, pore types, hydraulic flow units, and reservoir to non-reservoir (baffle or barrier) zones in the studied reservoir. Spatial facies distribution (facies variability) and development of disconformable surfaces (sequence boundaries) controlled the large-scale reservoir heterogeneities. On the other hand, variations in microscopic characteristics of depositional facies (i.e., textures and structures) and diagenetic features (e.g., intensity of dissolution, cementation and compaction) formed the small-scale reservoir heterogeneities. The best productive zones of this reservoir are meteorically-dissolved reef-talus and shoal facies, accumulated within the regressive systems tracts of third-order sequences. Intensively compacted and cemented facies formed the effective vertical barrier/baffle zones in this reservoir, especially within the transgressive systems tracts and around the maximum flooding surfaces. Results of this study revealed that the stratigraphic modified Lorenz plot (SMLP) is a useful tool for the discrimination of large-scale reservoir heterogeneities, while hydraulic flow units and Winland rock typing approaches are applicable for the identification of small-scale heterogeneities. This study indicated that the large-scale heterogeneities of carbonate reservoirs are predictable in a sequence stratigraphic framework, especially in the third-order scale. Such a heterogeneity model calibrated in the sequence stratigraphic framework provides a reliable basis for reservoir modeling of heterogeneous carbonate reservoirs in the field-to regional scales.

Time-lapse imaging of flow instability and rock heterogeneity impacts on CO2 plume migration in meter long sandstone cores

Our understanding and modelling of flow instabilities (e.g. viscous, gravitational) in the presence of geological heterogeneity is limited by a lack of experimental observations at relevant scales in consolidated media. Typically, small sample sizes (<50 mm diameter) restrict the scale of heterogeneity, and the development of unstable fingers may be damped by the sample boundaries. Furthermore, there is a lack of 3D dynamic data, resolved at a level to observe finger growth and interaction. To this end, we conduct a series of drainage & imbibition fluid-flow experiments with nitrogen and brine in meter-long, 100 mm-diameter Bentheimer and Boise sandstone samples using an advanced, high-pressure setup with medical X-Ray CT (XCT) imaging. Using novel beam-hardening and noise removal methods, we can resolve fluid distributions with a spatial resolution of 1.2 mm3 and temporal resolution of 90 s. The mobility (M) regime in each case is weakly unstable, M ≈ 0.5-5, with flow velocities equivalent to field scale injection rates. In the case of the weakly heterogeneous Bentheimer sample (porosity 0.26 ± 0.025), we find the plume migration is mainly controlled by buoyancy. On the other hand, the fluid distribution in the Boise sample is controlled by the strong unstructured heterogeneity (porosity 0.3 ± 0.1). The heterogeneity increases the gas trapping as less of the core is ultimately bypassed, and residual gas saturation increases behind local capillary barriers. We see little viscous instability in each experiment despite the unstable regime; structural heterogeneity and buoyancy largely control the flow under these field-relevant conditions. The meter scale 3D experimental dataset is provided open-access and represents an ideal benchmark to calibrate and improve multiphase flow models and upscaling methods.

Simulated treatment effects on bird communities inform landscape-scale dry conifer forest management.

Human land use and climate change have increased forest density and wildfire risk in dry conifer forests of western North America, threatening various ecosystem services, including habitat for wildlife. Government policy supports active management to restore historical structure and ecological function. Information on potential contributions of restoration to wildlife habitat can allow assessment of tradeoffs with other ecological benefits when prioritizing treatments. We predicted avian responses to simulated treatments representing alternative scenarios to inform landscape‐scale forest management planning along the Colorado Front Range. We used data from the Integrated Monitoring in Bird Conservation Regions program to inform a hierarchical multispecies occupancy model relating species occupancy and richness with canopy cover at two spatial scales. We then simulated changes in canopy cover (remotely sensed in 2018) under three alternative scenarios, (1) a “fuels reduction” scenario representing landscape‐wide 30% reduction in canopy cover, (2) a “restoration” scenario representing more nuanced, spatially variable treatments targeting historical conditions, and (3) a reference, no‐change scenario. Model predictions showed areas of potential gains and losses for species richness, richness of ponderosa pine forest habitat specialists, and the ratio of specialists to generalists at two (1 km2 and 250 m2) spatial scales. Under both fuels reduction and restoration scenarios, we projected greater gains than losses for species richness. Surprisingly, despite restoration more explicitly targeting ecologically relevant historical conditions, fuels reduction benefited bird species richness over a greater spatial extent than restoration, particularly in the lower montane life zone. These benefits reflected generally positive species associations with moderate canopy cover promoted more consistently under the fuels reduction scenario. In practice, contemporary forest management is likely to lie somewhere between the fuels reduction and restoration scenarios represented here. Therefore, our results inform where and how active forest management can best support avian diversity. Although our study raises questions regarding the value of including landscape‐scale heterogeneity as a management objective, we do not question the value of targeting finer scale heterogeneity (i.e., stand and treatment level). Rather, our results combined with those from previous work clarify the scale at which targeting structural heterogeneity and historical reference conditions can promote particular ecosystem services.

Orchid bees respond to landscape composition differently depending on the multiscale approach

ContextMultiscale approaches are essential for understanding ecological processes and detecting the scale of effect. However, nested multiscale approaches retain the effect of the landscape attributes from the smaller spatial scales into the larger ones. Thus, decoupling the nested scales can reveal detailed ecological responses to landscape context, but this multiscale approach is poorly explored.ObjectivesWe evaluated the scale of effect of the forest cover (%) and landscape heterogeneity on Euglossini bee communities combining coupled and decoupled multiscale approaches.MethodsThe Euglossini males were sampled in forest patches from 15 landscapes within the Atlantic Forest, southeast Brazil. For simplicity, we defined that the coupled approaches represented the local scales and decoupled the regional scales. We decoupled the scales by cutting out the smaller scales inserted into larger ones. We estimated the relationship of the bee community attributes with forest cover (%) and landscape heterogeneity in local and regional scales using Generalized Linear Models.ResultsWe found a trend of positive effects of landscape heterogeneity on species richness for decoupled regional scales. Forest cover and landscape heterogeneity on coupled local scales positively affected the Euglossini species abundance. The scale of effect for Euglossini species abundance was on coupled local scales.ConclusionsCombining coupled and decoupled multiscale approaches was essential to determine the scale of effect of the landscape composition on bee communities. Therefore, it is crucial to measure the influence of the landscape context on biodiversity. Maintaining landscapes with larger forest cover and spatial heterogeneity is important for bee requirements.

Automated porosity estimation using CT-scans of extracted core data

Estimation of porosity at a millimeter scale would be an order of magnitude finer resolution than traditional logging techniques. This enables proper description of reservoirs with thin layers and fine scale heterogeneities. To achieve this, we propose an end-to-end convolutional neural network (CNN) regression model that automatically predicts continuous porosity at a millimeter scale resolution using two-dimensional whole core CT scan images. More specifically, a CNN regression model is trained to learn from routine core analysis (RCA) porosity measurements. To characterize the performance of such approach, we compare the performance of this model with two linear regression models trained to learn the relationship between the average attenuation and standard deviation of the same two-dimensional images and RCA porosity. Our investigations reveal that the linear models are outperformed by the CNN, indicating the capability of the CNN model in extracting textures that are important for porosity estimations. We compare the predicted porosity results against the total porosity logs calculated from the density log. The obtained results show that the predicted porosity values using the proposed CNN method are well correlated with the core plug measurements and the porosity log. More importantly, the proposed approach can provide accurate millimeter scale porosity estimations, while the total porosity log is averaged over an interval and thus do not show such fine scale variations. Thus, the proposed method can be employed to calibrate the porosity logs, thereby reducing the uncertainties associated with indirect calculations of the porosity from such logs.

Towards the analysis of urban livability in China: spatial-temporal changes, regional types, and influencing factors.

The increasing drift of urbanization and its impact on urban human settlements are of major concern for China cities. Therefore, demystifying the spatial-temporal patterns, regional types, and affecting factors of urban livability in China is beneficial to urban planning and policy making regarding the construction of livable cities. In accordance with its connotation and denotation, this study develops a systematic evaluation and analysis framework for urban livability. Drawing on the panel data of 40 major cities in China from 2005 to 2019, an empirical research was further conducted. The results show that urban livability in China has exhibited a rising trend during the period, but this differs across dimensions. The levels of urban security and environmental health are lower than those of the three other dimensions. Spatially, cities with higher livability are mainly distributed in the first quadrant divided by the Hu Line and Bole-Taipei Line. Cities in the third quadrant are equipped with the lowest livability. In addition, the 40 major cities can be divided into five categories, and obvious differences exist in terms of the geographical distribution, overall livability level, and sub-dimensional characteristics of the different types. Furthermore, the results of the System GMM estimator indicate that the overall economic development exerts an inhibiting effect on the improvement of urban livability in present-day China, but this logical effect exhibits obvious heterogeneity in different time periods and diverse city scales. Finally, there are also differences in the influencing direction and degree of specific economic determinants.

Textual heterogeneity in massive sandstones from the Grès d'Annot and Numidian Flysch: Implications for depositional processes

ABSTRACT Vertical bed scale heterogeneity in six massive sandstone beds is investigated using digital image analysis to determine flow processes. Images parallel to the bedding plane and perpendicular to the apparent grain long-axis orientation were acquired to minimize the uncertainty in the grain-size and fabric, and increase the statistical significance of the data. Hypothesis testing was used to reduce the subjectivity in assigning vertical trends within each bed. Results show that a significant part of the deposit contains statistically significant vertical variation in grain-size and fabric (ρ-values &amp;lt; 0.01). At the bed scale, grain-size can be normally graded or non-graded. However, at shorter length scales, complex grading patterns emerge in the different percentiles of the grain-size distribution, including “oscillating,” “diverging,” and “converging” trends involving the fine- and coarse-tail percentile. Grain fabrics in the bedding-parallel sections show a consistently flow-oblique trend with an average azimuthal deviation of 48°, but becoming increasingly flow aligned at the tops of most beds. The majority of the bedding-perpendicular sections show imbrication angles &amp;gt; 15°, with an average imbrication angle of 78°. Both up- and down-current imbrication polarities are observed in equal abundance. These textural trends are interpreted as a product of active near-bed sedimentation processes that operate in high concentration near-bed layers, which develop at the base of concentrated, turbulent flows, and active fluidization processes that operate during sediment deposition. The short length-scale grain-size trends are likely a reflection of fluctuations in the sediment concentration in these near-bed layers and elutriation, which led to differential grain segregation processes influencing the different grain-size percentiles. The high shear stresses and frequent grain interaction in the near-bed layers, with concomitant fluidization, also generate the oblique bedding-parallel fabric and very high imbrication angles.

Mesoscale model and X-ray computed micro-tomographic imaging of damage progression in ultra-high-performance concrete

Predicting the mechanical performance of concrete from composition and processing remains a grand challenge. Our work focuses on predicting damage evolution in ultrahigh-performance concrete (UHPC), where analysis is challenging due to a lack of separation in the length scales of heterogeneity and a limited availability of data to describe the strength and failure of individual phases. Cement-hydration modeling informs segmentation of CT images from mechanical tests, providing a highly resolved 3-D representation of microstructure and damage evolution. Mesoscale simulations of damage in UHPC reveal how the relative strength of cement phases affects patterns of intra- and inter-granular fracture. We find that the nano-indentation hardness of individual phases is not necessarily a predictor of the relative shear strength needed to match experimentally observed fracture patterns. The mesoscale model predicts complicated material responses as emergent phenomena from relatively simple models of individual phases - a path towards forward modeling UHPC constitutive response.

Multifractal and joint multifractal analysis of soil invertebrate fauna, altitude, and organic carbon

ABSTRACT The objectives of this study were to evaluate the degree of multifractality of the spatial distribution of altitude, organic carbon concentration, and invertebrate fauna diversity, and to characterize the degree of joint multifractal association among these variables. Soil sampling was performed every 20 m across a 2,540 m transect, with a total of 128 sampling points in a sugarcane area in Goiana municipality, Pernambuco State. For each sampling point, the altitude, organic carbon concentration, and macrofauna diversity (diversity indices and functional groups) were evaluated. Spatial distributions of altitude, organic carbon concentration, and macrofauna diversity were characterized by the generalized dimension spectrum (Dq) and singularity spectrums [f(α) versus α], which presented multifractal behavior with different degrees of heterogeneity in scales. Joint multifractal analysis was useful for revealing the relationships at multiple scales between the studied variables, as demonstrated by the non-detected associations using traditional statistical methods. To quantify the spatial variability of edaphic fauna based on the multiple scales and association sets in the joint dimension, the impact of agricultural production systems on biological diversity can be described. All of the studied variables displayed a multifractal behavior with greater or lower heterogeneity degree depending on the variable, with altitude and organic carbon being the most homogeneous attributes.

Population density and spreading of COVID-19 in England and Wales.

We investigated daily COVID-19 cases and deaths in the 337 lower tier local authority regions in England and Wales to better understand how the disease propagated over a 15-month period. Population density scaling models revealed residual variance and skewness to be sensitive indicators of the dynamics of propagation. Lockdowns and schools reopening coincided with increased variance indicative of conditions with local impact and country scale heterogeneity. University reopening and December holidays reduced variance indicative of country scale homogenisation which reached a minimum in mid-January 2021. Homogeneous propagation was associated with better correspondence with normally distributed residuals while heterogeneous propagation was more consistent with skewed models. Skewness varied from strongly negative to strongly positive revealing an unappreciated feature of community propagation. Hot spots and super-spreading events are well understood descriptors of regional disease dynamics that would be expected to be associated with positively skewed distributions. Positively skewed behaviour was observed; however, negative skewness indicative of “cold-spots” and “super-isolation” dominated for approximately 8 months during the period of study. In contrast, death metrics showed near constant behaviour in scaling, variance, and skewness metrics over the full period with rural regions preferentially affected, an observation consistent with regional age demographics in England and Wales. Regional positions relative to density scaling laws were remarkably persistent after the first 5–9 days of the available data set. The determinants of this persistent behaviour probably precede the pandemic and remain unchanged.

High resolution species distribution and abundance models cannot predict separate shrub datasets in adjacent Arctic fjords

AbstractAimImproving species distribution models (SDMs) and species abundance models (SAMs) of woody shrubs is critical for predicting biodiversity changes in the Arctic, which is experiencing especially high warming rates. Yet, it remains relatively unexplored if SDMs and SAMs can explain local scale patterns. We aim to identify predictor differences for the distribution versus abundance of two widespread Arctic shrub species with high resolution models and to compare validation approaches to assess the models’ predictive abilities.LocationNuup Kangerlua (NK) and Kangerluarsunnguaq (K), two adjacent fjords in Southwest Greenland.MethodsWe conducted two separate field surveys in either fjord to construct high resolution (~90 m) SDMs and SAMs forBetula nanaandSalix glauca, analysing the predictive influences of local scale climate, topography and soil moisture indicators. We then alternatively trained and validated models in either NK or K fjord and compared these results with the common split‐sample validation approach. Finally, we assessed if including local scale biotic predictors improves SAM performance.ResultsTemperature extremes and precipitation best predicted the distributions of both species, whereas insolation and soil moisture indicators best predicted abundances. Compared to split‐sample validation, both SDM and SAM performance was substantially reduced with separate survey validation. Regardless of validation approach, models performed poor to moderately well, and including local scale biotic parameters improved SAM performance.Main conclusionsSubstantial differences in model performance between validation approaches highlight the usefulness of using a separate survey for validating model predictive performance. We discuss various factors that might have caused poor model performance, such as not capturing all relevant predictors or enough local scale heterogeneity in predictor or response variables. We emphasise the need to include predictors relevant at the spatial scale of study, such as local scale biotic interactions, for improved predictions at high spatial resolution.

A Lab on a Chip Experiment for Upscaling Diffusivity of Evolving Porous Media

Reactive transport modelling is a powerful tool to assess subsurface evolution in various energy-related applications. Upscaling, i.e., accounting for pore scale heterogeneities into larger scale analyses, remains one of the biggest challenges of reactive transport modelling. Pore scale simulations capturing the evolutions of the porous media over a wide range of Peclet and Damköhler number in combination with machine learning are foreseen as an efficient methodology for upscaling. However, the accuracy of these pore scale models needs to be tested against experiments. In this work, we developed a lab on a chip experiment with a novel micromodel design combined with operando confocal Raman spectroscopy, to monitor the evolution of porous media undergoing coupled mineral dissolution and precipitation processes due to diffusive reactive fluxes. The 3D-imaging of the porous media combined with pore scale modelling enabled the derivation of upscaled transport parameters. The chemical reaction tested involved the replacement of celestine by strontianite, whereby a net porosity increase is expected because of the smaller molar volume of strontianite. However, under our experimental conditions, the accessible porosity and consequently diffusivity decreased. We propose a transferability of the concepts behind the Verma and Pruess relationship to be applied to also describe changes of diffusivity for evolving porous media. Our results highlight the importance of calibrating pore scale models with quantitative experiments prior to simulations over a wide range of Peclet and Damköhler numbers of which results can be further used for the derivation of upscaled parameters.

Improving spatial characterization of buried bedrock valleys through airborne frequency-domain electromagnetic, residual magnetic, and surface resistivity measurements

The development of a three-dimensional (3D) hydrogeologic site model requires detailed knowledge of an area's hydrostratigraphy, information typically obtained from core logs, historical well records, outcrop mapping, and/or surface geophysics. Due to the high cost of drilling, a 3D model may rely on various statistical techniques to interpolate lithological boundaries between points located tens of kilometers or more apart. This situation can result in poorly constrained or simplified lithostratigraphic models, with under-sampled features such as karst and buried bedrock valleys. The non-invasive nature of airborne geophysical surveys combined with its dense sampling capability can fill critical data gaps. This study evaluates the capacity of a helicopter borne frequency-domain electromagnetic (FDEM) and residual magnetic survey using the Resolve™ system over a buried bedrock valley incised in a multi-layered dolostone aquifer within a complex glaciated environment in an urban/agricultural region of southern Ontario, Canada. The resulting airborne data was evaluated against two high-resolution ground-based electrical resistivity transects oriented orthogonal to the valley, alongside a published Quaternary geological model. A comparison between our local geophysically-based model and the regional geological model shows that the airborne electromagnetic surveys can markedly improve characterization of bedrock valley morphology and Quaternary sedimentary unit architecture that is comparable to that obtained using high-resolution surface-based electrical resistivity measurements with 10 m electrode spacing. The scale of heterogeneity that can be resolved by the airborne survey reveals the level of petrophysical variability that can be seen within the valley infill. Our study demonstrates the potential of airborne FDEM surveys in semi-urban agricultural environments where detailed characterization of Quaternary sediment architecture and bedrock topography is needed to inform 3D groundwater flow system variability.

Toxicological Implications of Biological Heterogeneity.

From a micro to macro scale of biological organization, macromolecular diversity and biological heterogeneity are fundamental properties of biological systems. Heterogeneity may result from genetic, epigenetic, and non-genetic characteristics (e.g., tissue microenvironment). Macromolecular diversity and biological heterogeneity are tolerated as long as the sustenance and propagation of life are not disrupted. They also provide the raw materials for microevolutionary changes that may help organisms adapt to new selection pressures arising from the environment. Sequence evolution, functional divergence, and positive selection of gene and promoter dosage play a major role in the evolution of life's diversity including complex metabolic networks, which is ultimately reflected in changes in the allele frequency over time. Robustness in evolvable biological systems is conferred by functional redundancy that is often created by macromolecular diversity and biological heterogeneity. The ability to investigate biological macromolecules at an increasingly finer level has uncovered a wealth of information in this regard. Therefore, the dynamics of biological complexity should be taken into consideration in biomedical research.

Entity aware sequence to sequence learning using LSTMs for estimation of groundwater contamination release history and transport parameters

Groundwater contaminant sources identification and parameter estimation using the simulation–optimization (S/O) approach require numerous runs of the computationally expensive simulation model through the optimization algorithm. The computational cost can be effectively reduced by using a surrogate model which can accurately approximate the simulation model. With the advent of deep learning, Long Short-Term Memory (LSTM) networks, which are suitable to learn sequential data, are being increasingly applied to regression problems involving time dependencies. However, for the simultaneous contaminant source identification and parameter estimation problem, the surrogate model requires to establish a relationship between release histories at the source locations to concentration measurements at observation points subject to given values of aquifer parameters. In this study, a novel deep neural network framework Entity Aware Sequence to sequence learning using Long Short-Term Memory (EAS-LSTM) is proposed as a surrogate model which takes both sequential i.e., release histories at the source locations at different stress periods and static variables i.e., transport parameters as inputs to predict breakthrough curves (BTCs) at observation points. The proposed surrogate model is applied to a heterogeneous field scale aquifer with 4 zones. The Mean Squared Error (MSE) using EAS-LSTM is 0.0033 ppm2 which is significantly better than that of Kriging and Support Vector Regression (SVR) which are 0.048 ppm2 and 1.302 ppm2 respectively. Comparison of EAS-LSTM model performance with Kriging and SVR based models demonstrates its higher accuracy. Further, the optimization algorithms for inverse modelling, the performances of Multiverse Optimizer (MVO), Grey wolf optimization (GWO) and Particle swarm optimization (PSO) are investigated. It is observed that the combination of EAS-LSTM and MVO provides better results in comparison to other surrogate simulation optimization (SSO) models.

Dual sea-level-climatic controls on the stratigraphic distribution of total organic carbon content and macerals in the Permian black shales of southwest Gondwana

The Whitehill Formation (WHF) is part of the organic-rich black shales deposited in the shallow seas that occurred within the continental interior of southwest Gondwana during the Lower Permian and has been identified as the main potential shale gas reservoir in South Africa. Here, high-resolution total organic carbon (TOC) content, organic petrographic composition, and multiple geochemical studies conducted on samples of the WHF in peak oil to gas maturity (vitrinite reflectance, Ro, range between 0.98 and 1.26%) were used to evaluate the influence of relative sea-level and climatic fluctuations on paleoproductivity, bottom-water redox condition, and clastic supply, and their combined control on the stratigraphic distribution of TOC content and macerals. Organic petrographic observations and geochemical data allowed recognition of three stratigraphic intervals (ORI-1 to ORI-3) that show marked differences in TOC content and maceral type, as well as mineralogical composition and submillimeter-to centimeter-scale sedimentary textures. TOC content increases from 0.93% at the base to 16.75% at the top. Data further revealed that the lower interval (ORI-1) is dominated by organic macerals biologically produced in the water column, including algal Tasmanites cysts, spores, and radiolaria, and depleted in land-derived macerals (inertinite, vitrinite). The mid (ORI-2) and upper ORI-3) intervals are shown to be composed of both production-derived components including bacterial biomass and aquatic plants (algae and radiolaria), as well as land-derived macerals, which are intermixed with clay-sized mineral particles, forming interconnected algal/bacterial mats. Trace and major element data indicate that these stratigraphic variations and heterogeneity of both TOC content and maceral reflect the variability of paleoproductivity, redox conditions, and clastic supply, which in turn were influenced by fluctuations of relative sea level and climate. Specifically, the accumulation of ORI-1 occurred during sea-level highstand and cool-climate, resulting in relatively higher paleoproductivity, reduced bottom-water oxygenation, and low clastic supply. However, pervasive degradation of organic matter at the seabed occasioned by low clastic detritus resulted in lower TOC content. Moreover, reduced supply of clastic detritus and severe organic matter degradation diminished the sizes of telalginite and the concentration of macerals into interconnected networks. On the contrary, intermittent decreases of relative sea-level (shallowing) and warmer conditions were detected in the mid and upper intervals, and the associated buildup of clastic sediment enhanced organic matter burial. Together these conditions resulted in higher TOC and better preservation of labile macerals. To conclude, the scale of stratigraphic heterogeneity of both TOC content and maceral type reported here may be present in many other black shale successions. Therefore, this type of study should constitute a fundamental step in the characterization of shales for their unconventional oil/gas reservoir potential.