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Special Issue "Advancing Complexity Research in Earth Sciences and Geography"

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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Earth Sciences and Geography".

Deadline for manuscript submissions: 31 October 2023 | Viewed by 22706

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Special Issue Editor

Prof. Dr. Jianbo Gao

E-Mail Website1 Website2
Guest Editor

Faculty of Geographical Sciences, Beijing Normal University, Beijing 100875, China
Interests: nonlinear dynamics; time series analysis; multiscale modeling; multiscale analysis of geophysical and geographic data; medical informatics; quantitative research on social, economic and financial complexity

Special Issue Information

Dear Colleagues,

Many complex phenomena in earth sciences and geography, including nonlinear fluid motions in the atmosphere, oceans, rivers, and lakes, coastal morphodynamics, volcanic and seismic activities, spatiotemporal dynamics of species, human movement trajectory, and city transportation dynamics, among many others, have played significant roles in the creation of complexity science, especially chaos theory and fractal geometry.With our increasing understanding of complex systems and availability of richer and more powerful methods for modeling complex systems, it is time to systematically examine the many complex phenomena in earth sciences and geography using state-of-the-art methods for modeling complex data. Undoubtedly, such efforts will invigorate research in earth sciences and geography and facilitate further development of complexity science. To help to achieve this goal, this Special Issue calls for papers that discuss which problems/phenomena in earth sciences and geography are complex, how they can be best solved/understood with which methods (or combination of methods) from complexity science, and whether new concepts and methods are needed to solve new problems that are closely related to the emergence of big data in earth sciences and geography. The topics of interest include but are not limited to:

Applications of complexity science in earth, ecological, and environmental sciences and geography;
Fractal and spatial–temporal long-range correlations;
Spatiotemporal data analysis;
Multiscale analysis;
Anomaly detection and precursor recognition in data;
Complexity and natural hazards;
Synthesizing methods from complexity science with AI-based techniques;
Emergent phenomena in earth sciences and geography, including emergent behaviors in technological innovations.

Prof. Jianbo Gao
Guest Editor

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Keywords

Complexity
chaos
fractal
long-range correlation
natural hazards
emergence

Published Papers (17 papers)

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Research

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Open AccessArticle

Ice Elevation Change Based on GNSS Measurements along the Korth-Traverse in Southern Greenland

Thomas Hitziger

Luisa Näke

and

Karel Pavelka

Appl. Sci. 2022, 12(23), 12066; https://doi.org/10.3390/app122312066 - 25 Nov 2022

Viewed by 485

Abstract

In 1912, a Swiss expedition led by meteorologist Alfred de Quervain crossed the Greenland ice sheet on a route from Disko Bay to Tasiilaq. Based on that, in 2002, a series of geodetic expeditions carried out by W. Korth and later by T. Hitziger began along the same traverse as in 1912, with the last measurements taken in May 2021. The statically collected GPS/GNSS data provide very accurate elevation changes at 36 points along the almost 700 km long crossing over a period of 19 years. According to this, there is a maximum increase of 2.1 m in the central area and a decrease of up to 38.7 m towards the coasts (influence Ilulissat Isbræ). By using kinematic GNSS measurements, there is a very dense profile with a spacing of a few meters. The comparison of those measurements is performed using crossing points or minimum distances and gives equivalent results for both methods. It is shown that local ice topography is preserved, and thus gaps in data sets can be caught. Areas of accumulation and ablation on the ice sheet can be identified, showing the widespread influence of outlet glaciers up to 200 km. The data can be used for direct verification of altimetry data, such as IceSat. Both IceSat elevations and their changes can be compared. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

A Comprehensive Study of Local, Global, and Combined Optimization Methods on Synthetic Seismic Refraction and Direct Current Resistivity Data

Panagiotis Kirmizakis

Pantelis Soupios

Emin Candansayar

Sherif Hanafy

and

Abdullatif Al-Shuhail

Appl. Sci. 2022, 12(22), 11589; https://doi.org/10.3390/app122211589 - 15 Nov 2022

Cited by 2 | Viewed by 630

Abstract

Most geophysical inversions face the problem of non-uniqueness, which poses a challenge in the mapping and delineation of the subsurface anomalies. To tackle this challenge, a combined local and global optimization approach is considered for jointly inverting two-dimensional direct current resistivity (DCR) and seismic refraction (SR) data that aim to estimate the corresponding physical model parameters. In this combined approach, the output of the local optimization method is used to determine the search space and tuning parameters for the global optimization algorithm. The multi-objective genetic algorithm (non-dominated sorting genetic algorithm) was utilized to jointly optimize the objective functions of two different methods. Because the genetic algorithm is a population-based optimization method, it requires numerous forward calculations. To deal with the expected high computational cost associated with this approach, parallel computing was utilized for the forward function evaluations to reduce the run time of the entire process. The proposed approach was tested using synthetic two-dimensional resistivity and velocity models that had three different types of anomalies (dyke, positive, and combined positive and negative). The results showed an improvement in the anomaly delineation in the output of the combined local and global optimization method compared with the local optimization method. Additionally, similar synthetic models were tested using only the single objective global optimization algorithm (conventional global optimization), which showed promising anomaly delineation. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Stochastic Modeling of the Al Hoceima (Morocco) Aftershock Sequences of 1994, 2004 and 2016

Jesús Galindo-Zaldívar

Carlos Sanz de Galdeano

and

Boyko Ranguelov

Appl. Sci. 2022, 12(17), 8744; https://doi.org/10.3390/app12178744 - 31 Aug 2022

Viewed by 641

Abstract

The three aftershock sequences that occurred in Al Hoceima, Morocco, in May 1994 (Mw 6.0), February 2004 (Mw 6.4) and January 2016 (Mw 6.3) were stochastically modeled to investigate their temporal and energetic behavior. A form of the restricted trigger model known as the restricted epidemic type aftershock sequence (RETAS) was used for the temporal analysis of the selected series. The best-determined fit models for each sequence differ based on the Akaike information criteria. The revealed discrepancies suggest that, although the activated fault systems are close (within 10 to 20 km), their stress regimes change and shift across each series. In addition, a stochastic model was presented to study the strain release following a specific strong earthquake. This model was constructed using a compound Poisson process and depicted the progression of the strain release during the aftershock sequence. The proposed model was then applied to the data. After the RETAS model was used to evaluate the behavior of the aftershock decay rate, the best-fit model was obtained and integrated into the strain-release stochastic analysis. By detecting the potential disparities between the observed data and model, the applied stochastic model of strain release allows for a more comprehensive examination. Furthermore, comparing the observed and expected cumulative energy release numbers revealed some variations at the start of all three sequences. This demonstrates that significant aftershock clusters occur more frequently shortly after the mainshock at the start of the sequence rather than if they are assumed to occur randomly. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

An Alternative Globalization Barometer for Investigating the Trend of Globalization

and

Appl. Sci. 2022, 12(15), 7896; https://doi.org/10.3390/app12157896 - 06 Aug 2022

Cited by 1 | Viewed by 885

Abstract

Analyzing, evaluating, and predicting the trend of globalization are highly valuable endeavors. However, existing literature lacks a quantifiable metric for objective evaluation. To fill the gap, we first compiled a Globalization Index based on existing globalization indices and using the CRITIC weighting method. Second, we constructed the Globalization Barometer and a trend term for trend analysis using the HP filtering method. Third, we conducted time-series predictions for globalization trajectory by applying the Random Forest model. Our results indicate that: (1) The de facto and de jure globalization both displayed a gradually upward trend over time; (2) the 2008 financial crisis and the 2020 COVID-19 pandemic negatively impacted globalization and served as turning points; (3) on a positive note, COVID-19 has narrowed the gap in both de facto and de jure globalization. This is due to the fact that the shocks were uneven, with economies that participated more in globalization weathering the brunt of the impact, while economies that participated less experiencing little changes; (4) the de facto and de jure globalization are predicted to remain on an upward trend for the subsequent 5 years. This research provides essential references for assessing and predicting globalization trends. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Seismic Imaging of Complex Velocity Structures by 2D Pseudo-Viscoelastic Time-Domain Full-Waveform Inversion

Niloofar Alaei

Mehrdad Soleimani Monfared

Amin Roshandel Kahoo

and

Thomas Bohlen

Appl. Sci. 2022, 12(15), 7741; https://doi.org/10.3390/app12157741 - 01 Aug 2022

Cited by 2 | Viewed by 711

Abstract

In the presented study, multi-parameter inversion in the presence of attenuation is used for the reconstruction of the P- and the S- wave velocities and the density models of a synthetic shallow subsurface structure that contains a dipping high-velocity layer near the surface with varying thicknesses. The problem of high-velocity layers also complicates selection of an appropriate initial velocity model. The forward problem is solved with the finite difference, and the inverse problem is solved with the preconditioned conjugate gradient. We used also the adjoint wavefield approach for computing the gradient of the misfit function without explicitly build the sensitivity matrix. The proposed method is capable of either minimizing the least-squares norm of the data misfit or use the Born approximation for estimating partial derivative wavefields. It depends on which characteristics of the recorded data—such as amplitude, phase, logarithm of the complex-valued data, envelope in the misfit, or the linearization procedure of the inverse problem—are used. It showed that by a pseudo-viscoelastic time-domain full-waveform inversion, structures below the high-velocity layer can be imaged. However, by inverting attenuation of P- and S- waves simultaneously with the velocities and mass density, better results would be obtained. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Incorporating a Topic Model into a Hypergraph Neural Network for Searching-Scenario Oriented Recommendations

Xin Huang

and

Xiaojuan Liu

Appl. Sci. 2022, 12(15), 7387; https://doi.org/10.3390/app12157387 - 22 Jul 2022

Viewed by 697

Abstract

The personalized recommendation system is a useful tool adopted by e-retailers to help consumers to find items in line with their preferences. Existing methods focus on learning user preferences from a user-item matrix or online reviews after purchasing, and they ignore the interactive features in the process of users’ learning about product information through search queries before they make a purchase. To this end, this study develops a topic augmented hypergraph neural network framework to predict the user’s purchase intention by connecting the latent topics embedded in a consumer’s online queries to their click, purchase, and online review behavior, which aims at mining the connection information existing in the interaction graph domain. Meanwhile, in order to reduce the influence of text noise words by fusing topic information, we integrate the topic distribution and convolutional embedding to better represent each user and item, which can make up for the lack of topic information in traditional convolutional neural networks. Extensive empirical evaluations on real-world datasets demonstrate that the proposed framework improves the novelty of recommendation items as well as accuracy. From a managerial perspective, recommending diversified and novel items to consumers may increase the users’ satisfaction, which is conducive to the sustainable development of e-commerce enterprises. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Would Forest Regrowth Compensate for Climate Change in the Amazon Basin?

Nafiseh Haghtalab

Nathan Moore

and

Pouyan Nejadhashemi

Appl. Sci. 2022, 12(14), 7052; https://doi.org/10.3390/app12147052 - 13 Jul 2022

Viewed by 1169

Abstract

Following potential reforestation in the Amazon Basin, changes in the biophysical characteristics of the land surface may affect the fluxes of heat and moisture behavior. This research examines the impacts of potential tropical reforestation on surface energy and moisture budgets, including precipitation and temperature. The study is novel in that while most studies look at the opposite driver (deforestation), this one examines the impact of potential forest rehabilitation on atmospheric behavior using WRF.V3.9 (weather research and forecast model). We found that forest rehabilitation across the Amazon Basin can make the atmosphere cooler with more moisture and latent heat (LH), especially during May-November. For instance, the mean seasonal temperature decreased significantly by about 1.2 °C, indicating the cooling effects of reforestation. Also, the seasonal precipitation increased by 5 mm/day in reforested areas. By reforestation, the mean monthly LH also increased as much as 50 W m⁻² in August in certain areas, while available moisture to the atmosphere increased by 27%, indicating possible causal mechanisms between increased LH and precipitation and emphasizing the mechanisms that were identified between the onset of the wet season and forest cover. Therefore, it is likely that forest regrowth across the basin leads to, if not reverses regional climate change, at least slowing down the rate of changes in the climate. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Investigating Limits in Exploiting Assembled Landslide Inventories for Calibrating Regional Susceptibility Models: A Test in Volcanic Areas of El Salvador

Chiara Martinello

Claudio Mercurio

Chiara Cappadonia

Miguel Ángel Hernández Martínez

Mario Ernesto Reyes Martínez

Jacqueline Yamileth Rivera Ayala

Christian Conoscenti

and

Edoardo Rotigliano

Appl. Sci. 2022, 12(12), 6151; https://doi.org/10.3390/app12126151 - 16 Jun 2022

Cited by 1 | Viewed by 915

Abstract

This research is focused on the evaluation of the reliability of regional landslide susceptibility models obtained by exploiting inhomogeneous (for quality, resolution and/or triggering related type and intensity) collected inventories for calibration. At a large-scale glance, merging more inventories can result in well-performing models hiding potential strong predictive deficiencies. An example of the limits that such kinds of models can display is given by a landslide susceptibility study, which was carried out for a large sector of the coastal area of El Salvador, where an apparently well-performing regional model (AUC = 0.87) was obtained by regressing a dataset through multivariate adaptive regression splines (MARS), including five landslide inventories from volcanic areas (Ilopango and Coatepeque caldera; San Salvador, San Miguel, and San Vicente Volcanoes). A multiscale validation strategy was applied to verify its actual predictive skill on a local base, bringing to light the loss in the predictive power of the regional model, with a lowering of AUC (20% on average) and strong effects in terms of sensitivity and specificity. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

In Search of the 1654 Seismic Source (Central Italy): An Obscure, Strong, Damaging Earthquake Occurred Less than 100 km from Rome and Naples

Luigi Cucci

and

Francesca R. Cinti

Appl. Sci. 2022, 12(3), 1150; https://doi.org/10.3390/app12031150 - 22 Jan 2022

Cited by 3 | Viewed by 1017

Abstract

The M6.3 earthquake that occurred in southern Lazio (Central Italy) in 1654 is the strongest seismic event to have occurred in the area. However, our knowledge about this earthquake is scarce and no study has been devoted to the individuation of its causative source. The main purpose of this study is putting together all of the information available for this shock to provide reliable landmarks to identify its seismic source. To this end, we present and discuss historical, hydrological, geological, and seismological data, both reviewed and newly acquired. An important, novel part of this study relies on an analysis of the coseismic hydrological changes associated with the 1654 earthquake and on the comparison of their distribution with models of the coseismic strain field induced by a number of potential seismogenic sources. We find more satisfactory results when imposing a lateral component of slip to the faults investigated. In particular, oblique left-lateral sources display a better fit between strain and hydrological signatures. Finally, the cross-analysis between the results from modeling and the other pieces of evidence collected point to the Sora fault, with its trend variability, as the probable causative source of the 1654 earthquake. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Normality in the Distribution of Revealed Comparative Advantage Index for International Trade and Economic Complexity

Bin Liu

and

Jianbo Gao

Appl. Sci. 2022, 12(3), 1125; https://doi.org/10.3390/app12031125 - 21 Jan 2022

Cited by 1 | Viewed by 1260

Abstract

The Revealed Comparative Advantage (RCA) index is an important metric for evaluating competitiveness of a country in exporting certain commodity. While it is desirable to have a normally distributed RCA index, the opposite is often found in empirical studies, and efforts for developing alternative indices of the RCA index have not been very successful. This motivates us to ask a more fundamental question: what is the significance of a normally distributed RCA index? To answer this question, we have defined a quantity called the Deviation from Gaussianity (DfG) based on the KS test, which quantifies the deviation of the distribution of a country’s RCA index from normality. By systematically analyzing the distribution characteristics of RCA index for each country from 1991 to 2019, we find that DfG is strongly negatively correlated with the logarithm of GDP and the Economic Complexity Index (ECI). In particular, correlation between DfG and GDP is stronger than that between ECI and GDP since 2008. These results suggest that DfG may serve as a new excellent index to quantify the economic complexity and economic performance of a country. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Towards Health Equality: Optimizing Hierarchical Healthcare Facilities towards Maximal Accessibility Equality in Shenzhen, China

Zhuolin Tao

Qi Wang

and

Wenchao Han

Appl. Sci. 2021, 11(21), 10282; https://doi.org/10.3390/app112110282 - 02 Nov 2021

Cited by 1 | Viewed by 1079

Abstract

Equal accessibility to healthcare services is essential to the achievement of health equality. Recent studies have made important progresses in leveraging GIS-based location–allocation models to optimize the equality of healthcare accessibility, but have overlooked the hierarchical nature of facilities. This study developed a hierarchical maximal accessibility equality model for optimizing hierarchical healthcare facilities. The model aims to maximize the equality of healthcare facilities, which is quantified as the variance of the accessibility to facilities at each level. It also accounts for different catchment area sizes of, and distance friction effects for hierarchical facilities. To make the optimization more realistic, it can also simultaneously consider both existing and new facilities that can be located anywhere. The model was operationalized in a case study of Shenzhen, China. Empirical results indicate that the optimal healthcare facility allocation based on the model provided more equal accessibility than the status quo. Compared to the current distribution, the accessibility equality of tertiary and secondary healthcare facilities in optimal solutions can be improved by 40% and 38%, respectively. Both newly added facilities and adjustments of existing facilities are needed to achieve equal healthcare accessibility. Furthermore, the optimization results are quite different for facilities at different levels, which highlights the feasibility and value of the proposed hierarchical maximal accessibility equality model. This study provides transferable methods for the equality-oriented optimization and planning of hierarchical facilities. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Weighted Centrality and Retail Store Locations in Beijing, China: A Temporal Perspective from Dynamic Public Transport Flow Networks

and

Appl. Sci. 2021, 11(19), 9069; https://doi.org/10.3390/app11199069 - 29 Sep 2021

Cited by 3 | Viewed by 1085

Abstract

The spatial relationship between transport networks and retail store locations is an important topic in studies related to commercial activities. Much effort has been made to study physical street networks, but they are seldom empirically discussed with considerations of transport flow networks from a temporal perspective. By using Beijing’s bus and subway smart card data (SCD) and point of interest (POI) data, this study examined the location patterns of various retail stores and their daily dynamic relationships with three weighted centrality indices in the networks of public transport flows: degree, betweenness, and closeness. The results indicate that most types of retail stores are highly correlated with weighted centrality indices. For the network constructed by total public transport flows in the week, supermarkets, convenience stores, electronics stores, and specialty stores had the highest weighted degree value. By contrast, building material stores and shopping malls had the weighted closeness and weighted betweenness values, respectively. From a temporal perspective, most retail types’ largest correlations on weekdays occurred during the after-work period of 19:00 to 21:00. On weekends, shopping malls and electronics stores changed their favorite periods to the daytime, while specialty stores favored the daytime on both weekdays and weekends. In general, the higher store type level of the shopping malls correlates more to weighted closeness or betweenness, and the lower-level store type of convenience stores correlates more to weighted degree. This study provides a temporal analysis that surpasses previous studies on street centrality and can help with urban commercial planning. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Modeling Snow Depth and Snow Water Equivalent Distribution and Variation Characteristics in the Irtysh River Basin, China

and

Appl. Sci. 2021, 11(18), 8365; https://doi.org/10.3390/app11188365 - 09 Sep 2021

Cited by 4 | Viewed by 1140

Abstract

Accurate simulation of snow cover process is of great significance to the study of climate change and the water cycle. In our study, the China Meteorological Forcing Dataset (CMFD) and ERA-Interim were used as driving data to simulate the dynamic changes in snow depth and snow water equivalent (SWE) in the Irtysh River Basin from 2000 to 2018 using the Noah-MP land surface model, and the simulation results were compared with the gridded dataset of snow depth at Chinese meteorological stations (GDSD), the long-term series of daily snow depth dataset in China (LSD), and China’s daily snow depth and snow water equivalent products (CSS). Before the simulation, we compared the combinations of four parameterizations schemes of Noah-MP model at the Kuwei site. The results show that the rainfall and snowfall (SNF) scheme mainly affects the snow accumulation process, while the surface layer drag coefficient (SFC), snow/soil temperature time (STC), and snow surface albedo (ALB) schemes mainly affect the melting process. The effect of STC on the simulation results was much higher than the other three schemes; when STC uses a fully implicit scheme, the error of simulated snow depth and snow water equivalent is much greater than that of a semi-implicit scheme. At the basin scale, the accuracy of snow depth modeled by using CMFD and ERA-Interim is higher than LSD and CSS snow depth based on microwave remote sensing. In years with high snow cover, LSD and CSS snow depth data are seriously underestimated. According to the results of model simulation, it is concluded that the snow depth and snow water equivalent in the north of the basin are higher than those in the south. The average snow depth, snow water equivalent, snow days, and the start time of snow accumulation (STSA) in the basin did not change significantly during the study period, but the end time of snow melting was significantly advanced. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

A Real-Time BLE/PDR Integrated System by Using an Improved Robust Filter for Indoor Position

and

Appl. Sci. 2021, 11(17), 8170; https://doi.org/10.3390/app11178170 - 03 Sep 2021

Cited by 4 | Viewed by 1289

Abstract

Indoor position technologies have attracted the attention of many researchers. To provide a real-time indoor position system with high precision and stability is necessary under many circumstances. In a real-time position scenario, gross errors of the Bluetooth low energy (BLE) fingerprint method are more easily occurring and the heading angle of the pedestrian will drift without acceleration and magnetic field compensation. A real-time BLE/pedestrian dead-reckoning (PDR) integrated system by using an improved robust filter has been proposed. In the PDR method, the improved Mahony complementary filter based on the pedestrian motion states is adopted to estimate the heading angle reducing the drift error. Then, an improved robust filter is utilized to detect and restrain the gross error of the BLE fingerprint method. The robust filter detected the gross error at different granularity by constructing a robust vector changing the observation covariance matrix of the extended Kalman filter (EKF) adaptively when the application is running. Several experiments are conducted in the true position scenario. The mean position accuracy obtained by the proposed method in the experiment is 0.844 m and RMSE is 0.74 m. Compared with the classic EKF, these two values are increased by 38% and 18%, respectively. The results show that the improved filter can avoid the gross error in the BLE method and provide high precision and scalability in indoor position service. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Facies Heterogeneity and Lobe Facies Multiscale Analysis of Deep-Marine Sand-Shale Complexity in the West Crocker Formation of Sabah Basin, NW Borneo

Muhammad Jamil

Numair Ahmed Siddiqui

Abdul Hadi Bin Abd Rahman

Noor Azahar Ibrahim

Mohd Suhaili Bin Ismail

and

Appl. Sci. 2021, 11(12), 5513; https://doi.org/10.3390/app11125513 - 15 Jun 2021

Cited by 9 | Viewed by 2839

Abstract

Deepwater lobes constitute a significant volume of submarine fans and are primarily believed to exhibit a simple sheet geometry. However, recent studies interpret the geometries of these deep-marine lobes as distinct with respect to the complexity of the facies and their distribution. Hence, a conceptual model of deep-marine sediments is essential to discuss the deep-marine sediments associated with the fan and lobe architecture. The present study highlights the facies heterogeneity and distribution of various lobe elements at a multiscale level by considering a case study of the West Crocker Formation of Sabah in northwest Borneo. The formation was logged on a bed-to-bed scale from recently well-exposed sections, with a total vertical thickness of more than 300 m. The lithological characteristics, bed geometry, sedimentary textures and structures of individual beds were used to categorize the rock units into nine sedimentary lithofacies: five sandstone lithofacies (S1–S5), one hybrid bed facies (H), two siltstone facies (Si1 and Si2) and one shale or mudstone facies (M). These facies were grouped into four facies associations (FA1–FA4), which were interpreted as lobe axis (FA1), lobe off-axis (FA2), lobe fringe (FA3) and distal fringe to interlobe (FA4) facies associations. This study is applicable for the distribution of lobes and their subseismic, multiscale complexities to characterize the potential of hydrocarbon intervals in deep-marine sand-shale system around the globe. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Open AccessArticle

Configurational Entropy for Optimizing the Encryption of Digital Elevation Model Based on Chaos System and Linear Prediction

Xinghua Cheng

and

Zhilin Li

Appl. Sci. 2021, 11(5), 2402; https://doi.org/10.3390/app11052402 - 08 Mar 2021

Cited by 2 | Viewed by 1244

Abstract

A digital elevation model (DEM) digitally records information about terrain variations and has found many applications in different fields of geosciences. To protect such digital information, encryption is one technique. Numerous encryption algorithms have been developed and can be used for DEM. A good encryption algorithm should change both the compositional and configurational information of a DEM in the encryption process. However, current methods do not fully take into full consideration pixel structures when measuring the complexity of an encrypted DEM (e.g., using Shannon entropy and correlation). Therefore, this study first proposes that configurational entropy capturing both compositional and configurational information can be used to optimize encryption from the perspective of the Second Law of Thermodynamics. Subsequently, an encryption algorithm based on the integration of the chaos system and linear prediction is designed, where the one with the maximum absolute configurational entropy difference compared to the original DEM is selected. Two experimental DEMs are encrypted for 10 times. The experimental results and security analysis show that the proposed algorithm is effective and that configurational entropy can help optimize the encryption and can provide guidelines for evaluating the encrypted DEM. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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Review

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Open AccessFeature PaperReview

Complex Systems, Emergence, and Multiscale Analysis: A Tutorial and Brief Survey

Jianbo Gao

and

Bo Xu

Appl. Sci. 2021, 11(12), 5736; https://doi.org/10.3390/app11125736 - 21 Jun 2021

Cited by 3 | Viewed by 2688

Abstract

Mankind has long been fascinated by emergence in complex systems. With the rapidly accumulating big data in almost every branch of science, engineering, and society, a golden age for the study of complex systems and emergence has arisen. Among the many values of big data are to detect changes in system dynamics and to help science to extend its reach, and most desirably, to possibly uncover new fundamental laws. Unfortunately, these goals are hard to achieve using black-box machine-learning based approaches for big data analysis. Especially, when systems are not functioning properly, their dynamics must be highly nonlinear, and as long as abnormal behaviors occur rarely, relevant data for abnormal behaviors cannot be expected to be abundant enough to be adequately tackled by machine-learning based approaches. To better cope with these situations, we advocate to synergistically use mainstream machine learning based approaches and multiscale approaches from complexity science. The latter are very useful for finding key parameters characterizing the evolution of a dynamical system, including malfunctioning of the system. One of the many uses of such parameters is to design simpler but more accurate unsupervised machine learning schemes. To illustrate the ideas, we will first provide a tutorial introduction to complex systems and emergence, then we present two multiscale approaches. One is based on adaptive filtering, which is excellent at trend analysis, noise reduction, and (multi)fractal analysis. The other originates from chaos theory and can unify the major complexity measures that have been developed in recent decades. To make the ideas and methods better accessed by a wider audience, the paper is designed as a tutorial survey, emphasizing the connections among the different concepts from complexity science. Many original discussions, arguments, and results pertinent to real-world applications are also presented so that readers can be best stimulated to apply and further develop the ideas and methods covered in the article to solve their own problems. This article is purported both as a tutorial and a survey. It can be used as course material, including summer extensive training courses. When the material is used for teaching purposes, it will be beneficial to motivate students to have hands-on experiences with the many methods discussed in the paper. Instructors as well as readers interested in the computer analysis programs are welcome to contact the corresponding author. Full article

(This article belongs to the Special Issue Advancing Complexity Research in Earth Sciences and Geography)

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