Spatial Version Research Articles

Local containment policies and countrywide spread of COVID-19 in the United States: an epidemiologic analysis

ABSTRACT We analyse the spatial diffusion of new COVID-19 cases and the countrywide impact of state-specific containment policies during the early months of the COVID-19 pandemic in the United States. We first use spatial econometric techniques to document direct and indirect spillovers of new infections across county and state lines, as well as the impact of individual states’ lockdown policies on infections in neighbouring states. We find consistent statistical evidence that new cases diffuse across county lines, holding county-level factors constant, and that the diffusion across counties was affected by the closure policies of adjacent states. We then develop a spatial version of the epidemiological susceptible–infected–recovered (SIR) model where new infections arise from interactions between infected people in one state and susceptible people in the same or in neighbouring states. We incorporate lockdown policies into our model and calibrate the model to match both the cumulative and the new infections across the 48 contiguous US states and DC. Our results suggest that had the states with the less restrictive social distancing measures tightened them by one level, the cumulative infections in other states would be about 5% smaller. In our spatial SIR model, the spatial containment policies such as border closures have a bigger impact on flattening the infection curve in the short-run than on the cumulative infections in the long-run.

Militarized Conflict, Trade, and Economic Development in a Structural Equation Model With Spatial Considerations

While conflict pervades virtually every aspect of society, there have been relatively few crossovers between Regional Science and Peace Science. This paper pays tribute to Walter Isard, pioneer of peace science research, and Kieran Donaghy, who has contributed to research on arms race dynamics and macroeconomic stability. We review studies that examine the impact of (i) trade on conflict, (ii) the economy on militarized disputes, and (iii) conflict on economic development. The analysis develops a structural equation model (SEM) to test the hypotheses simultaneously using the Correlates of War project data. Consistent with the liberal proposition, we found that the pacifying effect of trade is robust across alternative SEM specifications. Controlling for standard explanatory variables, the SEM estimates reveal that the indirect impact of economic development on conflict mediated by trade is statistically significant at the 0.1-percent level. Trade is, therefore, a critical intervening variable that transforms conflict-inducing economic expansion into a pacifying influence on militarized disputes. The spatial version of the SEM confirms that democracies do not attack each other. While trade does not appear to have a local spillover effect on conflict, proximity to neighboring democracies does lead to fewer conflicts. The final hypothesis argues that conflict affects national economic performance, which Donaghy refuted in his 1995 Conflict Management and Peace Science paper. Consistent with Donaghy’s finding, the estimates reject the claim that conflict disrupts economic stability. The conclusion section discusses the implications for Kieran Donaghy’s larger body of work.

On the Open-Endedness of Detecting Open-Endedness.

We argue that attempting to quantify open-endedness misses the point: The nature of open-endedness is such that an open-ended system will eventually move outside its current model of behavior, and hence outside any measure based on that model. This presents a challenge for analyzing Artificial Life systems, leading us to conclude that the focus should be on understanding the mechanisms underlying open-endedness, not simply on attempting to quantify it. To demonstrate this, we apply several measures to eight long experimental runs of the spatial version of the Stringmol automata chemistry. These experiments were originally designed to examine the hypothesis that spatial structure provides a defense against parasites. The runs successfully show this defense, but also show a range of innovative, and possibly open-ended, behaviors involved in countering a parasitic arms race. Commencing with system-generic measures, we develop and use a variety of measures dedicated to analyzing some of these innovations. We argue that a process of analysis, starting with system-generic measures but going on to system-specific measures, will be needed wherever the phenomenon of open-endedness is involved.

Assessing the reliability of web-based measurements of visual function

Many behavioural phenomena have been replicated using web-based experiments, but evaluation of the agreement between objective measures of web- and lab-based performance is required if scientists and clinicians are to reap the benefits of web-based testing. In this study, we investigated the reliability of a task which assesses early visual cortical function by evaluating the well-known ‘oblique effect’ (we are better at seeing horizontal and vertical edges than tilted ones) and the levels of agreement between remote, web-based measures and lab-based measures. Sixty-nine young participants (mean age, 21.8 years) performed temporal and spatial versions of a web-based, two-alternative forced choice (2AFC) orientation-identification task. In each case, orientation-identification thresholds (the minimum orientation difference at which a standard orientation could be reliably distinguished from a rotated comparison) were measured for cardinal (horizontal and vertical) and oblique orientations. Reliability was assessed in a subsample of 18 participants who performed the same tasks under laboratory conditions. Robust oblique effects were found, such that thresholds were substantially lower for cardinal orientations compared to obliques, for both web- and lab-based measures of the temporal and spatial 2AFC tasks. Crucially, web- and lab-based orientation-identification thresholds showed high levels of agreement, demonstrating the suitability of web-based testing for assessments of early visual cortical function. Future studies should assess the reliability of similar web-based tasks in clinical populations to evaluate their adoption into clinical settings, either to screen for visual anomalies or to assess changes in performance associated with progression of disease severity.

MI-STEG: A Medical Image Steganalysis Framework Based on Ensemble Deep Learning

Medical image steganography aims to increase data security by concealing patient-personal information as well as diagnostic and therapeutic data in the spatial or frequency domain of radiological images. On the other hand, the discipline of image steganalysis generally provides a classification based on whether an image has hidden data or not. Inspired by previous studies on image steganalysis, this study proposes a deep ensemble learning model for medical image steganalysis to detect malicious hidden data in medical images and develop medical image steganography methods aimed at securing personal information. With this purpose in mind, a dataset containing brain Magnetic Resonance (MR) images of healthy individuals and epileptic patients was built. Spatial Version of the Universal Wavelet Relative Distortion (S-UNIWARD), Highly Undetectable Stego (HUGO), and Minimizing the Power of Optimal Detector (MIPOD) techniques used in spatial image steganalysis were adapted to the problem, and various payloads of confidential data were hidden in medical images. The architectures of medical image steganalysis networks were transferred separately from eleven Dense Convolutional Network (DenseNet), Residual Neural Network (ResNet), and Inception-based models. The steganalysis outputs of these networks were determined by assembling models separately for each spatial embedding method with different payload ratios. The study demonstrated the success of pre-trained ResNet, DenseNet, and Inception models in the cover-stego mismatch scenario for each hiding technique with different payloads. Due to the high detection accuracy achieved, the proposed model has the potential to lead to the development of novel medical image steganography algorithms that existing deep learning-based steganalysis methods cannot detect. The experiments and the evaluations clearly proved this attempt.

Intermittent systemic exposure to lipopolysaccharide-induced inflammation disrupts hippocampal long-term potentiation and impairs cognition in aging male mice

Age-related cognitive decline, a common component of the brain aging process, is associated with significant impairment in daily functioning and quality of life among geriatric adults. While the complexity of mechanisms underlying cognitive aging are still being elucidated, microbial exposure and the multifactorial inflammatory cascades associated with systemic infections are emerging as potential drivers of neurological senescence. The negative cognitive and neurobiological consequences of a single pathogen-associated inflammatory experience, such as that modeled through treatment with lipopolysaccharide (LPS), are well documented. Yet, the brain aging impacts of repeated, intermittent inflammatory challenges are less well studied. To extend the emerging literature assessing the impact of infection burden on cognitive function among normally aging mice, here, we repeatedly exposed adult mice to intermittent LPS challenges during the aging period. Male 10-month-old C57BL6 mice were systemically administered escalating doses of LPS once every two weeks for 2.5 months. We evaluated cognitive consequences using the non-spatial step-through inhibitory avoidance task, and both spatial working and reference memory versions of the Morris water maze. We also probed several potential mechanisms, including cortical and hippocampal cytokine/chemokine gene expression, as well as hippocampal neuronal function via extracellular field potential recordings. Though there was limited evidence for an ongoing inflammatory state in cortex and hippocampus, we observed impaired learning and memory and a disruption of hippocampal long-term potentiation. These data suggest that a history of intermittent exposure to LPS-induced inflammation is associated with subtle but significantly impaired cognition among normally aging mice. The broader impact of these findings may have important implications for standard of care involving infections in aging individuals or populations at-risk for dementia.

An allometric prior enhances acoustic niche partitioning signal.

The acoustic niche hypothesis suggests that vocal signals of sympatric animal species are structured so as to minimize acoustic interference and facilitate communication. Accordingly, each species attempts to establish its own acoustic bandwidth so that intra-species signals are not masked. Detecting a non-random partitioning of the frequency spectrum among sympatric species could constitute evidence for the existence of acoustic avoidance behaviour. However, results from previous studies have been mixed or inconclusive, possibly as a consequence of overlooking the importance of physiological and ecological constraints. Here we introduce an improved test that incorporates prior information on body mass to account for the allometric correlation between mass (size) and vocalization frequency. By correcting for the bias induced by this correlation, the new test uncovers evidence of acoustic niche partitioning as a function of frequency in several tropical bird communities that would not be detected under a more standard test. Separately, we introduce a spatial version of the acoustic partitioning test which, in theory, could prove effective when data are collected from multiple sites located in close spatial proximity.

Examining the diagnostic utility of the mnemonic discrimination task for classification of cognition and amyloid‐β burden

AbstractBackgroundDeveloping non‐invasive diagnostics that can effectively predict amyloid‐β (Aβ) burden is crucial, as this technology could help to identify individuals with increased risk for AD and provide a window for potential treatment or intervention. Mnemonic discrimination tasks (MDTs) are designed to assess the ability to discriminate highly similar episodic memory representations (e.g., objects or spatial locations). The relationship between the MDT performance and Aβ burden, however, has not yet been directly tested. In this study, we investigated whether MDT performance can successfully classify cognitive status or Aβ load in older adults.MethodA sample of 85 older adults (60‐91 yrs old) with normal cognition and 9 older adults with a clinical diagnosis of Mild Cognitive Impairment (MCI) (62 ‐89 yrs old) underwent either cerebrospinal fluid (CSF) lumbar punctures or 18F‐florbetapir‐PET imaging. Cognitively normal individuals were stratified into Aβ+ and Aβ‐ subgroups based on quantitative thresholds for Aβ positivity (SUVR >1.11, CSF Ab1‐42/1‐40 < 0.062). Participants performed the object and spatial versions of the MDT. We used a random forest classification algorithm, including MDT performance metrics and demographic factors (age, gender, education) as features to classify cognitive status (normal vs. MCI) and Aβ status (positive vs. negative). We evaluated the area under the receiver operating characteristic curve (AUC) using the classifiers in a leave‐one‐out cross validation design.ResultFor classification of cognitive status, the best model performance was achieved by including all the features of object and spatial MDT metrics and demographics (AUC = 0.80). For classification of Aβ status in cognitively normal participants, the best model performance was also achieved by including all the features of object and spatial MDT metrics and demographics (AUC = 0.65).ConclusionOur findings suggest that MDT performance measures may have clinical utility as a diagnostic tool to predict cognitive status and in vivo Aβ burden in preclinical older adults.

Entorhinal-Hippocampal Circuit Integrity Is Related to Mnemonic Discrimination and Amyloid-β Pathology in Older Adults.

Mnemonic discrimination, a cognitive process that relies on hippocampal pattern separation, is one of the first memory domains to decline in aging and preclinical Alzheimer's disease. We tested whether functional connectivity (FC) within the entorhinal-hippocampal circuit, measured with high-resolution resting state fMRI, is associated with mnemonic discrimination and amyloid-β (Aβ) pathology in a sample of 64 cognitively normal human older adults (mean age, 71.3 ± 6.4 years; 67% female). FC was measured between entorhinal-hippocampal circuit nodes with known anatomical connectivity, as well as within cortical memory networks. Aβ pathology was measured with 18F-florbetapir-PET, and neurodegeneration was assessed with subregional volume from structural MRI. Participants performed both object and spatial versions of a mnemonic discrimination task outside of the scanner and were classified into low-performing and high-performing groups on each task using a median split. Low object mnemonic discrimination performance was specifically associated with increased FC between anterolateral entorhinal cortex (alEC) and dentate gyrus (DG)/CA3, supporting the importance of this connection to object memory. This hyperconnectivity between alEC and DG/CA3 was related to Aβ pathology and decreased entorhinal cortex volume. In contrast, spatial mnemonic discrimination was not associated with altered FC. Aβ was further associated with dysfunction within hippocampal subfields, particularly with decreased FC between CA1 and subiculum as well as reduced volume in these regions. Our findings suggest that Aβ may indirectly lead to memory impairment through entorhinal-hippocampal circuit dysfunction and neurodegeneration and provide a mechanism for increased vulnerability of object mnemonic discrimination.SIGNIFICANCE STATEMENT Mnemonic discrimination is a critical episodic memory process that is performed in the dentate gyrus (DG) and CA3 subfield of the hippocampus, relying on input from entorhinal cortex. Mnemonic discrimination is particularly vulnerable to decline in older adults; however, the mechanisms behind this vulnerability are still unknown. We demonstrate that object mnemonic discrimination impairment is related to hyperconnectivity between the anterolateral entorhinal cortex and DG/CA3. This hyperconnectivity was associated with amyloid-β pathology and neurodegeneration in entorhinal cortex, suggesting aberrantly increased network activity is a pathological process. Our findings provide a mechanistic explanation of the vulnerability of object compared to spatial mnemonic discrimination in older adults and has translational implications for choice of outcome measures in clinical trials for Alzheimer's disease.

Spatial organisation plasticity reduces disease infection risk in rock–paper–scissors models

We study a three-species cyclic game system where organisms face a contagious disease whose virulence may change by a pathogen mutation. As a responsive defence strategy, organisms’ mobility is restricted to reduce disease dissemination in the system. The impact of the collective self-preservation strategy on the disease infection risk is investigated by performing stochastic simulations of the spatial version of the rock–paper–scissors game. Our outcomes show that the mobility control strategy induces plasticity in the spatial patterns with groups of organisms of the same species inhabiting spatial domains whose characteristic length scales depend on the level of dispersal restrictions. The spatial organisation plasticity allows the ecosystems to adapt to minimise the individuals’ disease contamination risk if an eventual pathogen alters the disease virulence. We discover that if a pathogen mutation makes the disease more transmissible or less lethal, the organisms benefit more if the mobility is not strongly restricted, thus forming large spatial domains. Conversely, the benefits of protecting against a pathogen causing a less contagious or deadlier disease are maximised if the average size of groups of individuals of the same species is significantly limited, reducing the dimensions of groups of organisms significantly. Our findings may help biologists understand the effects of dispersal control as a conservation strategy in ecosystems affected by epidemic outbreaks.

Transient Turing patterns in a morphogenetic model

One of the most surprising mechanisms to explain the symmetry breaking phenomenon linked to pattern formation is known as Turing instabilities. These patterns are self-organising spatial structures resulting from the interaction of at least two diffusive species in specific conditions. The ideas of Turing have been used extensively in the specialised literature both to explain developmental patterns, as well as synthetic biology design. In the present work we study a previously proposed morphogenetic synthetic circuit consisting of two genes controlled by the same regulatory system. The spatial homogeneous version of this simple model presents a rich phase diagram, since it has a saddle-node bifurcation, spirals and limit cycle. Linear stability analysis and numerical simulations of the complete model allow us to determine the conditions for the development of Turing patterns, as well as transient patterns. We found that the parameter region where Turing patterns are found is much smaller than the region where transient patterns occur. We observed that the temporal evolution towards Turing patterns can present one or two different length scales, depending on the initial conditions. Further, we found a parameter region where the persistence time of the transient patterns depends on the distance between the parameters values on which the system is operating and the boundary of Turing patterns. This persistence time has a singularity at a critical distance that gives place to metastable patterns. To the best of our knowledge, transient and metastable patterns associated with Turing instabilities have not been previously reported in morphogenetic models.

An Extension of the Mixed Effects Model that Includes Correlation within Group Errors

Mixed effect models allow for a great deal of flexibility in defining random outcomes, but they limit within-group errors to independent disbursed random variables with a zero mean and constant variance. In addition to its random outcomes for the implied structure, this model is expanded in this paper by including within-group correlated errors. We demonstrate how to accurately predict the model's parameters using a marginal maximum probability (ML) method. The model's accuracy is demonstrated by a real-world example. Additionally, we provide several instructions for correlation systems to represent serial and spatial correlation. Finally, we define how to combine variance features and correlation systems to flexibly model the within-group variance-covariance shape. We also discuss how the lme function can be used to maintain the prolonged linear mixed effects model. And, when compared to other models, the exponential spatial correlation version has the smallest AIC and BIC, making it appear to be the best within-group correlation model.

Spatially inhomogeneous populations with seed-banks: II. Clustering regime

We consider a spatial version of the classical Moran model with seed-banks where the constituent populations have finite sizes. Individuals live in colonies labelled by Zd, d≥1, playing the role of a geographic space, carry one of two types, ♡ or ♠, and change type via resampling as long as they are active. Each colony contains a seed-bank into which individuals can enter to become dormant, suspending their resampling until they exit the seed-bank and become active again. Individuals resample not only from their own colony, but also from other colonies according to a symmetric random walk transition kernel. The latter is referred to as migration. The sizes of the active and the dormant populations depend on the colony and remain constant throughout the evolution.It was shown in den Hollander and Nandan (2021) that the spatial system is well-defined, admits a family of equilibria parametrised by the initial density of type ♡, and exhibits a dichotomy between clustering (mono-type equilibrium) and coexistence (multi-type equilibrium). This dichotomy is determined by a clustering criterion that is given in terms of the dual of the system, which consists of a system of interacting coalescing random walks. In this paper we provide an alternative clustering criterion, given in terms of an auxiliary dual that is simpler than the original dual, and identify a range of parameters for which the criterion is met, which we refer to as the clustering regime. It turns out that if the sizes of the active populations are non-clumping (i.e., do not take arbitrarily large values in finite regions of the geographic space) and the relative strengths of the seed-banks (i.e., the ratio of the sizes of the dormant and the active population in each colony) are bounded uniformly over the geographic space, then clustering prevails if and only if the symmetrised migration kernel is recurrent.The spatial system is hard to analyse because of the interaction in the original dual and the inhomogeneity of the colony sizes. By comparing the auxiliary dual with a non-interacting two-particle system, we are able to control the correlations that are caused by the interactions. The work in den Hollander and Nandan (2021) and the present paper is part of a broader attempt to include dormancy into interacting particle systems.

Validation of the low dissipation computational algorithm CABARET-MFSH for multilayer hydrostatic flows with a free surface on the lock-release experiments

The article presents the results of validation in lock-release experiments of a new low-dissipative multilayer hydrostatic model CABARET-MFSH. This model describes the dynamics of fluid with variable density and free surface. The computational algorithm of the new model is based on the method of hyperbolic decomposition, which presents of a multilayer structure in the form of separate layers interacting throughout interfaces. An explicit CABARET scheme is used to solve the system of hyperbolic equations in each layer. The scheme has a second order of approximation and is time reversible. Consequently, the scheme is initially non-dissipative. Besides, mass and momentum exchange between layers and filtering of flux variables are used to regularize the multilayer hydrostatic model, which introduces additional numerical dissipation into the scheme. The parameters of the filtering procedure are determined empirically from the condition of the stability of the algorithm and the minimal of the scheme viscosity. A 2D spatial version of our model is used for modeling lock-release experiments described in this article due to the uniformity of the current in the transverse direction. The results of numerical calculations are in satisfactory agreement with experimental data.

BVI-CC: A Dataset for Research on Video Compression and Quality Assessment

The video technology scenery has been very vivid over the past years, with novel video coding technologies introduced that promise improved compression performance over state-of-the-art technologies. Despite the fact that a lot of video datasets are available, representative content of the wide parameter space along with subjective evaluations of variations of encoded content from an unpartial end is required. In response to this requirement, this paper features a dataset, the BVI-CC. Three video codecs were deployed to create the variations of the encoded sequences: High Efficiency Video Coding Test Model (HM), AOMedia Video 1 (AV1), and Versatile Video Coding Test Model (VTM). Nine source video sequences were carefully selected to offer both diversity and representativeness in the spatio-temporal domain. Different spatial resolution versions of the sequences were created and encoded by all three codecs at pre-defined target bit rates. The compression efficiency of the codecs was evaluated with commonly used objective quality metrics, and the subjective quality of their reconstructed content was also evaluated through psychophysical experiments. Additionally, an adaptive bit rate (convex hull rate-distortion optimization across spatial resolutions) test case was assessed using both objective and subjective evaluations. Finally, the computational complexities of the tested codecs were examined. All data have been made publicly available as part of the dataset, which can be used for coding performance evaluation and video quality metric development.

Анализ структурных сдвигов: обзор пространственных версий

The article presents the genesis of spatial shift-share analysis. The author focuses on the links of spatial versions of shift-share analysis with the classical one, which is not just a starting point for them from a historical point of view, but retains the status of their constituent element. The author describes spatial versions that integrate spatially lagged variables into the classical version and keep the national economy as a reference, and a version that is used in conjunction with the classical one in terms of content and defines the economy of the neighborhood of the region as a reference. For each spatial version, the total number of alternative structural decompositions and the number of decompositions that provide estimates of only simple industry mix and competitive effects with a transparent interpretation are defined. It’s shown that the development of a spatial shift-share analysis is driven by the idea of using multi-contextual framework (national, neighborhood and regional) for studying models of regional economic growth. The author describes the logic of the shift-share analysis, carried out in accordance with the spatial version, which introduces an autonomous identity for each level of the spatial hierarchy. A brief review of papers devoted to the study of economic growth models of Russian regions based on the shift-share analysis, including its spatial versions, is given, and a prospect for developing the results obtained in these papers is proposed

Enhancing SRTM digital elevation models with deep-learning-based super-resolution image generation

Digital elevation models are responsible for providing altimetric information on a surface to be mapped. While global models of low and medium spatial resolution are available open source by several space agencies, the high-resolution ones, which are utilized in scales 1:25,000 and larger, are scarce and expensive. Here we address this limitation by the utilization of deep learning algorithms coupled with SISR techniques in digital elevation models to obtain better spatial quality versions from lower resolution inputs. The development of a GAN-based methodology enables the improvement of the initial spatial resolution of low-resolution images. A dataset with different pairs of digital elevation models was created with the objective of allowing the study to be carried out, promoting the emergence of new research groups in the area as well as enabling the comparison between the results obtained. It has been found that by increasing the number of iterations the performance of the generated model was improved and the quality of the generated image increased. Furthermore, the visual analysis of the generated image against the high- and low-resolution ones showed a great similarity between the first two.

Структурные сдвиги в отраслях ТЭК Дальнего Востока: пространственный анализ

This paper assesses the effects of the investment shock to the fuel and energy sectors of the Far East within the framework of the Eastern vector of Russia’s energy policy. We systemize the basic investment projects implemented in these sectors in order to increase export supplies of fuel and energy resources to the Asia-Pacific countries and assess the shifts in the structure of fuel and energy resources production in the region over the period from 2012 to 2019. In calculations, we use the classical and spatial versions of the method of structural decomposition of the growth rates of the sectoral indicators (shift-share analysis). On the basis of structural decomposition of the changes on the average annual number of employees and the GVA of the fuel and energy complex of the Far East at different spatial levels (national, macroregional (interregional) and regional) we draw the following conclusions: a strong impulse for development in the framework of the Eastern vector of Russia’s energy policy was given to extractive industries, their competitive effects were positive and overrode the negative shocks, which were destabilizing the national economy; an important factor in the economic dynamics of the extractive industries of the fuel and energy complex were gains/losses in inter-regional competition, but not spatial externalities; in the oil and gas industry, unlike in the coal industry, there is potential for formation of spatial externalities; developing faster than others, extractive industries remain enclaves in regional economies

Implementation and future challenges of seismotectonic mapping system for earthquake emergency response

Earthquake emergency response maps represent a set of geographical maps related to the seismic activity, geological tectonic background, assessment of economy and population losses, secondary disaster probability, as well as other information for emergency response and rescue. It requires producing these kinds of maps as quickly as possible after an earthquake. The seismotectonic map is one of the most important maps for earthquake emergency response. In this study, we developed an earthquake emergency response system to produce the map automatically. This paper introduces a intelligent system framework using spatial analysis, version management and web communication technology for emergency response, data maintenance, remote map revision, version management and statistics, which is not discussed in previous studies. The system based on MySQL, ArcGIS platform, python, and curl. It can detect the earthquake fast report proactive. When capturing the official report, it activated the response function module. The system achieved the first version mapping, fault data pushing, and earthquake catalogue updating by data-driven method automatically and rapidly. The mapping process can be divided into map layout and expert knowledge. The expert knowledge part can be divided into a geography map and explanatory note. The system performed well on the map layout and geography map. The explanatory note in the map, which introduces regional tectonics and expert knowledge, can provide the nearest fault’s name, active age, and feature in the first version. Experts need to produce the second version manually. The fault data of 5°×5°area centred on the main shock is clipped and sent to a specified mailbox. The system added the earthquake parameters to the database. The result shows that this system finished these work well automatically and reduce the responds time and data maintenance time. If the earthquake causes serious calamity, seismogeology experts need to collect, summarize and analyse literature, document, and other information for successor versions and even predicting aftershock and disaster. It is practically needed to build a more intelligent and efficient system to produce a successor version of the map and push more knowledge in the future. The challenge in the future is to develop a more intelligent system to self-explain the seismotectonic map and push expert knowledge. This goal can only be achieved by processing expert knowledge data like regional lithology, tectonic structure, paleo earthquake, and historical earthquake in the database layer, data access layer, and business logic layer of the system.

Proposal of an apposite strategy-updating rule for the vaccination game where hubs refer to hubs and lower-degree agents refer to lower-degree agents.

In the vaccination game, the spread of disease and the human decision-making to obtain pre-emptive vaccinations are coordinately united in successive seasons. This is backed both by epidemiological models, such as SIR, and by evolutionary game theory, assuming a given strategy-updating rule. Several rules have been proposed by the community and rely on either the imitation concept or the switching-action concept. The latter directly stipulates whether or not an agent commits to a course of action based on a rule, such as the aspiration concept. In contrast, the former borrowed its fundamental idea from the spatial version of a two-player, two-strategy (2 × 2) game, such as the spatial prisoner's dilemma (SPD). The pairwise Fermi (PW-Fermi) strategy has been heavily employed as the most representative idea. The present study modifies PW-Fermi, which consists of two processes: one for selecting a pairwise opponent to imitate and the other giving the probability of copying from the opponent. Instead of a random selection, our proposed model applies a stochastically skewed selection in which a neighbor who has a similar degree to the focal player is preferentially selected. This specific rule allows us to establish a quite efficient society, in which hub agents spontaneously obtain vaccination, but lower-degree agents do not. To this end, a small number of higher-degree agents, who are exposed to higher infection risk, are urged to be vaccinated, whereas many other agents enjoy free-riding. This produces a relatively small vaccination cost as a social sum and also effectively suppresses the spread of disease, resulting in a small disease cost for society as a whole.