One of the main issues in the application of statistical‐learning‐based methods to the characterization of hydrological phenomena is the complex parameterization of the high‐quality reconstruction. ...The difficulty of obtaining enough training data and generating multiple‐scale structures hinder the applications of deep‐learning‐based methods in hydrogeological modeling. Hydrogeological modeling problems can be regarded as stochastic processes, and we propose a novel hydrogeological modeling approach based on convolutional conditional neural processes (GM‐ConvCNPs), a meta‐learning approach for dealing with stochastic processes. In this work, GM‐ConvCNP is used to reconstruct the entire spatial structures of subsurface hydrological attributes and channels from a limited amount of conditioning data. To achieve 3‐D characterization of subsurface structures, 3‐D GM‐ConvCNP is proposed according to the spatial distribution characteristics of 3‐D conditioning data. Compared to other deep‐learning‐based methods, we use a small amount of training data and obtain positive model generalization capabilities. Four data sets of categorical and continuous hydrogeological structures are exploited in the experiments. Various validation tests including variograms, connectivity functions, and multi‐dimensional scaling (MDS) map are used to evaluate the quality of generated realizations. The proposed approach is able to significantly reduce training consumption and improve the performance of realizations compared to a set of different benchmark tests. The experiments confirm that the GM‐ConvCNP model can extract heterogeneous patterns by using meta‐learning from limited training data and reconstruct multiple‐scale hydrogeological structures. A case study demonstrates that our method can be applied to characterize multiple‐source hydrological variables.
Plain Language Summary
Characterization of realistic subsurface structures can provide a prior reference for understanding geosystem behavior in spatial scale. Hydrogeological modeling is a well‐established method of describing the structure of subsurface spaces. One of the main issues in the application of statistical‐learning‐based methods to the characterization of hydrological phenomena is the complex parameterization of the high‐quality reconstruction. Insufficient amount of training data has become a hindrance to the application of deep‐learning‐based hydrogeological modeling methods. In this work, we propose a novel method to reconstruct the entire spatial structures of subsurface hydrogeological attributes and channels from a limited amount of conditioning data, named geological modeling method with convolutional conditional neural process (GM‐ConvCNP). The proposed approach is able to significantly reduce training consumption and improve the performance of realizations compared to a set of different benchmark tests. Experimental results confirm that the GM‐ConvCNP model can extract heterogeneous patterns by using meta‐learning from limited training data and reconstruct multiple‐scale hydrogeological structures. We show that can be applied to characterize multiple‐source hydrological variables.
Key Points
A meta‐learning‐based geological modeling method with convolutional conditional neural process is proposed to characterize various 2‐D and 3‐D hydrogeological structures
Multiple‐scale realizations can be obtained by using the same trained model with single‐scale training data
The proposed method can reproduce realizations with both categorical facies and continuous variables
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Multiple-point geostatistical (MPS) simulation methods have attracted an enormous amount of attention in earth and environmental sciences due to their ability to enhance extraction and synthesis of ...heterogeneous patterns. To characterize the subtle features of heterogeneous structures and phenomena, large-scale and high-resolution simulations are often required. Accordingly, the size of simulation grids has increased dramatically. Since MPS is a sequential process for each grid unit along a simulation path, it results in severe computational consumption. In this work, a new hybrid parallel framework is proposed for the case of MPS simulation on large areas with enormous amount of grid cells. Both inter-node-level and intra-node-level parallel strategies are combined in this framework. To maintain the quality of the realizations, we implement a conflict control method adapting to the Monte-Carlo process. Also, an optimization method for the simulation information is embedded to reduce the inter-node communication overhead. A series of synthetic tests were used to verify the availability and performance of the proposed hybrid parallel framework. The results corroborate that the proposed framework can efficiently achieve the high-resolution reproduction and characterization of complex structures and phenomena in earth sciences.
•A hybrid parallel framework for MPS is implemented on Tianhe-2 supercomputer.•A multiple-supervisors coordinating parallel strategy is implemented.•An improved conflict control strategy for the Monte-Carlo process is exploited.•Simulation information transform interface for inter-node communication optimization is performed.•A fine-grained parallel method with regional division strategy is proposed.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•Ultrasonic/microwave-extracted green coffee oil showed the highest cafestol, kahweol and α-tocopherol contents.•The oil extracted from green coffee beans with the microwave technique presented a ...higher content of polyunsaturated fatty acids.•The highest content of γ-tocopherol and β-sitosterol was reported for green coffee oil derived by pressurized liquid extraction.•Data fusion combined with chemometrics was effective in evaluating green coffee oil obtained by different extraction techniques.
In this study, ultrasonic/microwave-assisted extraction (UMAE), microwave-assisted extraction (UAE), ultrasound-assisted extraction (UAE), and pressurized liquid extraction (PLE) were applied to extract green coffee oil (GCO), and the physicochemical indexes, fatty acids, tocopherols, diterpenes, and total phenols as well as antioxidant activity of GCO were investigated and compared. The results indicated that the extraction yield of UMAE was the highest (10.58 ± 0.32%), while that of PLE was the lowest (6.34 ± 0.65%), and linoleic acid and palmitic acid were the major fatty acids in the GCO, ranging from 40.67% to 43.77% and 36.57% to 38.71%, respectively. A large proportion of fatty acids and phytosterols were not significantly influenced by the four extraction techniques. However, tocopherols, diterpenes, total phenols, and the free radical scavenging activity were significantly different among these four GCOs. Moreover, structural changes in the coffee residues were explored by scanning electron microscopy and Fourier transform infrared spectroscopy. Overall, the high antioxidant activity of GCO demonstrated that it can be used as a highly economical natural product in the food and agricultural industries.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Low‐bandgap mixed tin–lead perovskite solar cells (PSCs) have been attracting increasing interest due to their appropriate bandgaps and promising application to build efficient all‐perovskite tandem ...cells, an effective way to break the Shockley–Queisser limit of single‐junction cells. Tin fluoride (SnF2) has been widely used as a basis along with various strategies to improve the optoelectronic properties of low‐bandgap SnPb perovskites and efficient cells. However, fully understanding the roles of SnF2 in both films and devices is still lacking and fundamentally desired. Here, the functions of SnF2 in both low‐bandgap (FASnI3)0.6(MAPbI3)0.4 perovskite films and efficient devices are unveiled. SnF2 regulates the growth mode of low‐bandgap SnPb perovskite films, leading to highly oriented topological growth and improved crystallinity. Meanwhile, SnF2 prevents the oxidation of Sn2+ to Sn4+ and reduces Sn vacancies, leading to reduced background hole density and defects, and improved carrier lifetime, thus largely decreasing nonradiative recombination. Additionally, the F− ion preferentially accumulates at hole transport layer/perovskite interface with high SnF2 content, leading to more defects. This work provides in‐depth insights into the roles of SnF2 additives in low‐bandgap SnPb films and devices, assisting in further investigations into multiple additives and approaches to obtain efficient low‐bandgap PSCs.
In‐depth insights into the roles of tin fluoride (SnF2) additive in low‐bandgap mixed tin (Sn)‐lead (Pb) perovskite and efficient solar cells are provided. The growth mode of the film, highly oriented topological growth, and reduced Sn2+ oxidation are achieved via proper SnF2 doping. Additionally, the accumulation of F− at hole transport layer/perovskite interface is shown at higher SnF2 content, leading to more defects.
Full text
Available for:
FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Increased availability and quality of surface and subsurface observations provide the chance to improve the perception of hydrological phenomena. An important factor that hinders our understanding of ...hydrological structures is the characterization of heterogeneous patterns with continuous attributes inside the structures (e.g., porosity, permeability, fluid saturation, etc.). Unlike categorical attributes, continuous attributes convey more realistic characteristics but require more computational resources to characterize such complex earth systems. In this work, we propose a novel deep learning approach for the characterization of complex hydrological realism with continuous attributes based on generative adversarial networks (GANs) and self‐attention mechanism, named SA‐RelayGANs. To address the complexity of heterogeneous hydrological structures, we divide the modeling process into two stages: facies construction stage and property reconstruction stage. In the first stage, we employ an improved GAN with self‐attention mechanism to construct the heterogeneous structures while adhering to hard conditioning constraints. In the second stage, we utilize another GAN with an attribute enhancement term to reconstruct realizations based on the constructed structures and observations. SA‐RelayGANs can successfully predict the statistical distributions of heterogeneous structures with continuous attributes by using limited observations. This study highlights the effectiveness of using GANs to characterize the heterogeneous patterns of hydrological realism and the application over large geoscience fields.
Key Points
A novel deep learning approach is proposed for the characterization of complex hydrological structures with continuous attributes
The two relaying generative adversarial networks (GANs) are dedicated to facies construction and property reconstruction respectively
SA‐RelayGANs makes it possible to accurate characterize the subsurface realism from sparse observations
Full text
Available for:
BFBNIB, FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Multiple-point statistics (MPS) has shown promise in representing complicated subsurface structures. For a practical three-dimensional (3-D) application, however, one of the critical issues is the ...difficulty in obtaining a credible 3-D training image. However, bidimensional (2-D) training images are often available because established workflows exist to derive 2-D sections from scattered boreholes and/or other samples. In this work, we propose a locality-based MPS approach to reconstruct 3-D geological models on the basis of such 2-D cross sections (3DRCS), making 3-D training images unnecessary. Only several local training subsections closer to the central uninformed node are used in the MPS simulation. The main advantages of this partitioned search strategy are the high computational efficiency and a relaxation of the stationarity assumption. We embed this strategy into a standard MPS framework. Two probability aggregation formulas and their combinations are used to assemble the probability density functions (PDFs) from different subsections. Moreover, a novel strategy is adopted to capture more stable PDFs, where the distances between patterns and flexible neighborhoods are integrated on multiple grids. A series of sensitivity analyses demonstrate the stability of the proposed approach. Several hydrogeological 3-D application examples illustrate the applicability of the 3DRCS approach in reproducing complex geological features. The results, in comparison with previous MPS methods, show better performance in portraying anisotropy characteristics and in CPU cost.
The Spatiotemporal Weighted Regression (STWR) model is an extension of the Geographically Weighted Regression (GWR) model for exploring the heterogeneity of spatiotemporal processes. A key feature of ...STWR is that it utilizes the data points observed at previous time stages to make better fit and prediction at the latest time stage. Because the temporal bandwidths and a few other parameters need to be optimized in STWR, the model calibration is computationally intensive. In particular, when the data amount is large, the calibration of STWR becomes heavily time-consuming. For example, with 10,000 points in 10 time stages, it takes about 2307 s for a single-core PC to process the calibration of STWR. Both the distance and the weighted matrix in STWR are memory intensive, which may easily cause memory insufficiency as data amount increases. To improve the efficiency of computing, we developed a parallel computing method for STWR by employing the Message Passing Interface (MPI). A cache in the MPI processing approach was proposed for the calibration routine. Also, a matrix splitting strategy was designed to address the problem of memory insufficiency. We named the overall design as Fast STWR (F-STWR). In the experiment, we tested F-STWR in a High-Performance Computing (HPC) environment with a total number of 204,611 observations in 19 years. The results show that F-STWR can significantly improve STWR's capability of processing large-scale spatiotemporal data.
•A parallel method (F-STWR) for the Spatiotemporal Weighted Regression (STWR).•A matrix splitting approach is developed for memory saving in STWR.•F-STWR significantly improves STWR's capability of processing large-scale data.•F-STWR extends the scope of STWR for various research topics in the real world.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Magnetic iron oxide nanoparticles (MIONPs) are particularly attractive in biosensor, antibacterial activity, targeted drug delivery, cell separation, magnetic resonance imaging tumor magnetic ...hyperthermia, and so on because of their particular properties including superparamagnetic behavior, low toxicity, biocompatibility, etc. Although many methods had been developed to produce MIONPs, some challenges such as severe agglomeration, serious oxidation, and irregular size are still faced in the synthesis of MIONPs. Thus, various strategies had been developed for the surface modification of MIONPs to improve the characteristics of them and obtain multifunctional MIONPs, which will widen the applicational scopes of them. Therefore, the processes, mechanisms, advances, advantages, and disadvantages of six main approaches for the synthesis of MIONPs; surface modification of MIONPs with inorganic materials, organic molecules, and polymer molecules; applications of MIONPs or modified MIONPs; the technical challenges of synthesizing MIONPs; and their limitations in biomedical applications were described in this review to provide the theoretical and technological guidance for their future applications.
Full text
Available for:
CEKLJ, EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Microgrid as an important part of smart grid comprises distributed generators (DGs), adjustable loads, energy storage systems (ESSs) and control units. It can be operated either connected with the ...external system or islanded with the support of ESSs. While the daily output of DGs strongly depends on the temporal distribution of natural resources such as wind and solar, unregulated electric vehicle (EV) charging demand will deteriorate the unbalance between the daily load curve and generation curve. In this paper, a statistic model is presented to describe daily EV charging/discharging behaviors considering the randomness of the initial state of charge (SOC) of EV batteries. The optimization problem is proposed to obtain the economic operation for the microgrid based on this model. In day-ahead scheduling, with the estimated power generation and load demand, the optimal charging/discharging scheduling of EVs during 24 h is achieved by serial quadratic programming. With the optimal charging/discharging scheduling of EVs, the daily load curve can better track the generation curve. The network loss in grid-connected operation mode and required ESS capacity in islanded operation mode are both decreased.
In recent years, nanoporous thin films are widely studied as an effective way to improve the thermoelectric performance or manipulate the thermal transport within thin-film-based devices. In ...practice, nanoporous patterns can effectively cut off the heat flow and thus guide the thermal transport along the desired direction. However, a better design of these thermal devices is not addressed, such as thermal cloaking as the thermal counterpart for optical invisibility cloaks. In existing designs based on the Fourier's law, composite materials with varied structures are often introduced to achieve the required location-dependent thermal conductivities to distort the heat flux. At the micro-to nano-scale, such designs are difficult to be implemented and factors such as the interfacial thermal resistance must be further considered. In this work, inverse thermal designs of a nanoporous thin film are used to achieve the two-dimensional thermal cloaking, without introducing any other variation of the composition or material to tune the local thermal conductivity. This simple approach can be widely used for thin-film-based devices to protect heat-sensitive regions or function as thermal camouflaging devices. The proposed nanoporous structures can also be used to tune the local properties of a thin film for general applications, such as graded thermoelectric materials.
Display omitted
•Nanoporous patterns to replace nanocomposites for locally tuned thermal properties.•General approach to achieve thermal cloaking within thin-film devices.•Extension to thermal camouflage and graded thermoelectric devices.•Inverse thermal design to find suitable nanoporous patterns.
Full text
Available for:
GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
PDF
