Integrating chemistry, fluid flow, and mechanics to drive spontaneous formation of three-dimensional (3D) patterns in anchored microstructures.

Enzymatic reactions in solution drive the convection of confined fluids throughout the enclosing chambers and thereby couple the processes of reaction and convection. In these systems, the energy released from the chemical reactions generates a force, which propels the fluids' spontaneous motion. Here, we use theoretical and computational modeling to determine how reaction-convection can be harnessed to tailor and control the dynamic behavior of soft matter immersed in solution. Our model system encompasses an array of surface-anchored, flexible posts in a millimeter-sized, fluid-filled chamber. Selected posts are coated with enzymes, which react with dissolved chemicals to produce buoyancy-driven fluid flows. We show that these chemically generated flows exert a force on both the coated (active) and passive posts and thus produce regular, self-organized patterns. Due to the specificity of enzymatic reactions, the posts display controllable kaleidoscopic behavior where one regular pattern is smoothly morphed into another with the addition of certain reactants. These spatiotemporal patterns also form "fingerprints" that distinctly characterize the system, reflecting the type of enzymes used, placement of the enzyme-coated posts, height of the chamber, and bending modulus of the elastic posts. The results reveal how reaction-convection provides concepts for designing soft matter that readily switches among multiple morphologies. This behavior enables microfluidic devices to be spontaneously reconfigured for specific applications without construction of new chambers and the fabrication of standalone sensors that operate without extraneous power sources.

Production of Noble-Metal Nanohelices Based on Nonlinear Dynamics in Electrodeposition of Binary Copper Alloys.

Spatiotemporal pattern formation is dynamic self-organization widely observed in nature and drives various functions. Among these functions, chirality plays a central role. The relationship between dynamic self-organization and chirality has been an open question; therefore, the production of chiral nanomaterials by dynamic self-organization has not been achieved. Here, we show that the confinement of a two-dimensional spatiotemporal micropattern via the electrodeposition of a binary Cu alloy into a nanopore induces mirror symmetry breaking to produce a helical nanostructure of the noble-metal component although it is still not yet possible to control the handedness at this stage. This result suggests that spatiotemporal symmetry breaking functions as a mirror symmetry breaking if cylindrical pores are given as the boundary condition. This study can be a model system of how spatiotemporal symmetry breaking plays a role in mirror symmetry breaking, and it proposes a new approach to producing helical nanomaterials through dynamic self-organization.

Analysis of the spatiotemporal patterns and decoupling effects of China's water resource spatial equilibrium.

The significance of water resource spatial equilibrium (WRSE) research is to maximally remove the spatial restrictions of water on regional development, including social development, economic development and eco-environmental maintenance. Although great achievements have been made, national-scale WRSE research is rare; besides, the spatiotemporal patterns and decoupling effects of WRSE have been poorly studied in current research. Thus, the aim of this research is to measure the WRSE in China for the period 2008-2019 by using an improved coupling coordination model and to empirically analyse its distribution dynamics and decoupling effects. The results show that the WRSE status of China's 31 provincial administrative regions from 2008 to 2019 is at a moderate level. Based on the spatiotemporal patterns and decoupling effects analysis, areas in urgent need of improving WRSE status are identified, and tailored countermeasures are provided for each area. To our knowledge, this paper is the first nationwide study of the spatiotemporal patterns and decoupling effects of WRSE.

Land cover diversity: Reshaping the socioeconomic value of land.

The land is a vital resource to support economic growth. Previous studies have mainly taken urban and agricultural areas as the major factors of economic boost. However, the supporting role of diversified land in sustainable economy has not been fully valued. This study analyzed the relationship between land cover diversity and socioeconomic outputs from 1992 to 2019 on up to 2086 counties in China. The results showed the regional disparity in the eastern and western counties. In the western counties, land cover diversity was positively correlated with per area socioeconomic indicators throughout the period. In the eastern counties, the correlation with per area indicators has gradually changed from negative to positive. The correlation with per capita indicators was more significant in the east than west. The regional disparity and temporal trends may come from the influence of water resources, demographic pressure and other constraints in different regions. The refreshed land value can help to achieve sustainable development and improve human well-being in the long term.

Evaluation and prediction of ecological carrying capacity in the Qilian Mountain National Park, China.

With increasing human impacts on the ecosystem in natural protected areas, there is an urgent need to undertake an assessment of ecological carrying capacity taken as a benchmark for assessing regional sustainability. Based on satellite remote sensing and socio-economic statistical data from 2000 to 2019, this study distinguished the controlling factors for the spatial and temporal patterns of ecological carrying capacity in the Qilian Mountain National Park, one of the 10 pilot national parks in China. The ecological carrying capacity index (ECCI) was developed by using the Driver-Pressure-State-Impact-Response framework and a comprehensive weight method. The results showed that the multiyear averaged ECCI was low in the south and west but was high in central and eastern regions. The spatial distribution of the ECCI was constrained by soil resources, ecosystem quality, land use/cover and water environment. At the regional scale, the ECCI decreased from 2000 to 2014, especially in Tianzhu, where farmland expansion and severe droughts reduced habitat quality and ecosystem function. However, the ECCI increased significantly from 2014 to 2019, which was attributed to a warm moist climate and the implementation of eco-environmental protection policies. Forest and grassland coverage, soil and water conservation, waste water treatment amount and terrestrial water reserves accounted for 35%, 26%, 20% and 8%, respectively, of the temporal variability in the ECCI. Concurrent with national park development, the ECCI is predicted to increase in most areas from 2020 to 2029 by back-propagation artificial neural networks, except for Sunan, Shandan and Menyuan, possibly owing to increasing conflicts between humans and the environment. The findings of this study provide evidence about the effectiveness of government policies in promoting regional sustainability by altering ecosystem composition and function. In addition, the dominant drivers for the temporal variability of ecological carrying capacity varied in space according to stepwise regression analysis, calling for region-specific management strategies in mountain protected areas and their surroundings.

Spatiotemporal pattern of Covid-19 outbreak in Turkey.

The earliest case of Covid-19 was documented in Wuhan city of China and since then the virus has been spreading throughout the globe. The aim of this study is to evaluate the clusters of Covid-19 among the provinces in Turkey and to examine whether the clustering pattern has changed after the country's lockdown strategy. The spatial dependence of Covid-19 in 81 provinces of Turkey was examined by spatial analysis between February 8 and June 28, 2021. Global and Local Moran's I and Gi* were employed to measure the global and local spatial autocorrelation degrees. The geographical distribution of Covid-19 in the provinces of Turkey showed a strong spatial autocorrelation while the spatial structure of the clusters varied by weeks. The findings of the study show that the complete lockdown carried out in Turkey has been quite effective in mitigating Covid-19. The importance of spatial relations in preventing the spread of the disease in Turkey has also been demonstrated in this context.

Visualizing the spatiotemporal pattern of yolk sac membrane vascular network by enhanced local fractal analysis.

OBJECTIVE: To establish methods for providing a comprehensive and detailed description of the spatial distribution of the vascular networks, and to reveal the spatiotemporal pattern of the yolk sac membrane vascular network during the angiogenic procedure. METHODS: Addressing the limitations in the conventional local fractal analysis, an improved approach, named scanning average local fractal dimension, was proposed. This method was conducted on 6 high-resolution vascular images of the yolk sac membrane for 3 eggs at two stages (E3 and E4) to characterize the spatial distribution of the complexity of the vascular network. RESULTS: With the proposed method, the spatial distribution of the complexity of the yolk sac membrane vascular network was visualized. From E3 to E4, the local fractal dimension increased in 3 eggs, 1.80 ± 0.02 vs. 1.85 ± 0.02, 1.72 ± 0.03 vs. 1.83 ± 0.02, and 1.77 ± 0.03 vs. 1.82 ± 0.02, respectively. The mean local fractal dimension in the most distal area from the embryo proper was the lowest at E3 while the highest at E4. At E3, the most peaks of the local fractal dimension were located in the vein territories and shifted to artery territories at E4. CONCLUSIONS: The spatial distribution of the complexity of the yolk sac membrane vascular network exhibited diverse patterns at different stages. In addition from E3 to E4, the increment of complexity at the intersection areas between arteries and sinus terminalis was with the most advance. This is consistent with the physiologic evidence. The present work provides a potential approach for investigating the spatiotemporal pattern of the angiogenic process.

Decomposing the spatial and temporal effects of climate on bird populations in northern European mountains.

The relationships between species abundance or occurrence versus spatial variation in climate are commonly used in species distribution models to forecast future distributions. Under "space-for-time substitution", the effects of climate variation on species are assumed to be equivalent in both space and time. Two unresolved issues of space-for-time substitution are the time period for species' responses and also the relative contributions of rapid- versus slow reactions in shaping spatial and temporal responses to climate change. To test the assumption of equivalence, we used a new approach of climate decomposition to separate variation in temperature and precipitation in Fennoscandia into spatial, temporal, and spatiotemporal components over a 23-year period (1996-2018). We compiled information on land cover, topography, and six components of climate for 1756 fixed route surveys, and we modeled annual counts of 39 bird species breeding in the mountains of Fennoscandia. Local abundance of breeding birds was associated with the spatial components of climate as expected, but the temporal and spatiotemporal climatic variation from the current and previous breeding seasons were also important. The directions of the effects of the three climate components differed within and among species, suggesting that species can respond both rapidly and slowly to climate variation and that the responses represent different ecological processes. Thus, the assumption of equivalent species' response to spatial and temporal variation in climate was seldom met in our study system. Consequently, for the majority of our species, space-for-time substitution may only be applicable once the slow species' responses to a changing climate have occurred, whereas forecasts for the near future need to accommodate the temporal components of climate variation. However, appropriate forecast horizons for space-for-time substitution are rarely considered and may be difficult to reliably identify. Accurately predicting change is challenging because multiple ecological processes affect species distributions at different temporal scales.

Exploration on the coordinated development of urbanization and the eco-environmental system in central China.

Scientific evaluation of the interaction between urbanization and the eco-environmental system in Central China is of great significance. To optimizing the quality of urbanization and improving the eco-environmental state. As a case study of Central China, this research attempts to build a set of analytical systems to realize the integrated deconstruction from analyzing comprehensive evaluation indexes, quantification of coupling coordination, spatiotemporal evolution traits, decoupling path exploration to influential factor analysis. It tries to clarify the differences between cities, identify problematic areas, and propose targeted improvement measures. The outcomes show that the urbanization level of the cities in Central China has been improved significantly. In contrast, their eco-environmental levels are fluctuating, with the growth rate lower than that of urbanization. The coordination level between the two systems is rising, changing from primary dysfunction to intermediate coordination. The coordination level is characterized by obvious spatial association dominated by Types H-H and L-L and ever-increasing agglomeration. The decoupling between the two systems only falls into two types: strong decoupling and relative decoupling with expansion, indicating a negative effect between them. There is a problem regarding negative urbanization development. The factors including energy consumption, investment in fixed assets, opening to the outside world, technological progress, and government management capabilities all have an impact on the coordination of the two with divergent significances.

Spatiotemporal analysis of highly pathogenic avian influenza (H5N1) outbreaks in poultry in Egypt (2006 to 2017).

BACKGROUND: In Egypt, the highly pathogenic avian influenza (HPAI) subtype H5N1 is endemic and possesses a severe impact on the poultry. To provide a better understanding of the distributional characteristics of HPAI H5N1 outbreaks in Egypt, this study aimed to explore the spatiotemporal pattern and identify clusters of HPAI H5N1 outbreaks in Egypt from 2006 to 2017. RESULTS: The Epidemic curve (EC) was constructed through time series analysis; in which six epidemic waves (EWs) were revealed. Outbreaks mainly started in winter peaked in March and ended in summer. However, newly emerged thermostable clades (2.2.1.1 and 2.2.1.2) during the 4th EW enabled the virus to survive and cause infection in warmer months with a clear alteration in the seasonality of the epidemic cycle in the 5th EW. The endemic situation became more complicated by the emergence of new serotypes. As a result, the EC ended up without any specific pattern since the 6th EW to now. The spatial analysis showed that the highest outbreak density was recorded in the Nile Delta considering it as the 'Hot spot' region. By the 6th EW, the outbreak extended to include the Nile valley. From spatiotemporal cluster epidemics, clustering in the Delta was a common feature in all EWs with primary clusters consistently detected in the hot-spot region, but the location and size varied with each EW. The highest Relative Risk (RR) regions in an EW were noticed to contain the primary clusters of the next EW and were found to include stopover sites for migratory wild birds. They were in Fayoum, Dakahlia, Qalyobiya, Sharkia, Kafr_Elsheikh, Giza, Behera, Menia, and BeniSuef governorates. Transmission of HPAI H5N1 occurred from one location to another directly resulted in a series of outbreaks forming neighboring secondary clusters. The absence of geographical borders between the governorates in addition to non-restricted movements of poultry and low vaccination and surveillance coverage contributed to the wider spread of infection all over Egypt and to look like one epidemiological unit. CONCLUSION: Our findings can help in better understanding of the characteristics of HPAI H5N1 outbreaks and the distribution of outbreak risk, which can be used for effective disease control strategies.

The spatiotemporal response of China's vegetation greenness to human socio-economic activities.

Climate change and human socioeconomic activities both strongly impact long-term vegetation greenness. It is more a challenge to evaluate the impacts of socioeconomic activities on vegetative greenness than climate change, partially due to the lack of appropriate quantitative indicators of the former. Here we examined the relationship between the remote sensing nighttime light (NTL) data and the Normalized Difference Vegetation Index (NDVI), which in this study are used as the proxies of socioeconomic activities and vegetation greenness, respectively. We first eliminated the vegetation greenness changes in response to climate change and calculated the human-activities-induced NDVI (HNDVI). After explored the spatiotemporal patterns of the HNDVI and NTL data across China from 1998 to 2018, we studied the relationship between the HNDVI and NTL at the grid and county levels, respectively. Our results show that the mean adjusted DN values of the NTL data (NTLI) continuously increase (+0.2938) across our study area from 1998 to 2018, whereas the HNDVI values fluctuate with a general upward trend (+0.0018). Most grids (91.2%) with increased HNDVI were found in rural areas, particularly in the Northeast forest shelterbelt and the Loess Plateau. By contrast, the HNDVI values in rapidly urbanized areas in Chinese major urban agglomerations mainly show a downward trend, especially in the Yangtze River Delta (YRD) urban agglomeration. The relationships between the NTLI and HNDVI are inconsistent over time and across space, which could be attributed to land use conditions, afforestation projects in rural areas, and greening activities in urban areas over different periods and regions.

China's ecosystem service value in 1992-2018: Pattern and anthropogenic driving factors detection using Bayesian spatiotemporal hierarchy model.

Maintaining ecosystem services (ESs) and reducing ecosystem degradation are important goals for achieving sustainable development. However, under the influence of various anthropogenic factors, the total ecosystem service value (ESV) of China continues to decline, and the detailed processes involved in this decline are unclear. In this paper, a new long-term annual land cover dataset (the Climate Change Initiative Land Cover or CCI-LC dataset) with a spatial resolution of 300 m was employed to estimate the ESV of China, and Bayesian spatiotemporal hierarchy models were built to examine the detailed patterns and anthropogenic driving factors. From 1992 to 2018, the total ESV of China fluctuated and decreased from 3265.3 to 3253.29 billion US$ at an average rate of 0.55 billion US$ per year. Furthermore, the model revealed the spatiotemporal variations in the ESV pattern, and simultaneously detected the influences of 9 variables related to economic factors, population, infrastructure, energy, agriculture and ecological restoration, providing a convenient and effective method for ESV spatiotemporal analysis. The results enrich our understanding of the detailed spatiotemporal variation and anthropogenic driving factors underlying the declining ESV in China. These findings have substantial guiding implications for adjusting ecological regulation policies.

COVID-19 lockdown introduces human mobility pattern changes for both Guangdong-Hong Kong-Macao greater bay area and the San Francisco bay area.

In response to the coronavirus disease 2019 (COVID-19) pandemic, various countries have sought to control COVID-19 transmission by introducing non-pharmaceutical interventions. Restricting population mobility, by introducing social distancing, is one of the most widely used non-pharmaceutical interventions. Although similar population mobility restriction interventions were introduced, their impacts on COVID-19 transmission are often inconsistent across different regions and different time periods. These differences may provide critical information for tailoring COVID-19 control strategies. In this paper, anonymized high spatiotemporal resolution mobile-phone location data were employed to empirically analyze and quantify the impact of lockdowns on population mobility. Both the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China and the San Francisco Bay Area (SBA) in the United States were studied. In response to the lockdowns, a general reduction in population mobility was observed, but the structural changes in mobility are very different between the two bays: 1) GBA mobility decreased by approximately 74.0-80.1% while the decrease of SBA was about 25.0-42.1%; 2) compared to SBA, the GBA had smoother volatility in daily volume during the lockdown. The volatility change indexes for GBA and SBA were 2.55% and 7.52%, respectively; 3) the effect of lockdown on short- to long-distance mobility was similar in GBA while the medium- and long-distance impact was more pronounced in SBA.

Land-use transition and its driving forces in a minority mountainous area: a case study from Mao County, Sichuan Province, China.

Land-use change is an important research topic in global environmental change. Analyzing land-use change and its driving factors can aid in the evaluation of the current and the determination of future land-use policies. This study took Mao County, Southwest China, as the study area and used the land-use change and statistical data surveyed in 2009 and 2019. With the help of geographic information system technology, a land-use transfer matrix was used to comprehensively analyze the characteristics of spatiotemporal differentiation of land use, while the driving mechanism was analyzed by constructing the influencing factors using a geographical detector model. The results showed that the change in land use in Mao County was drastic. The increasing land types included orchards, grasslands, built-up lands, and water bodies, whereas the decreasing land types included croplands, forestlands, and unused lands. The main driving factors of land-use transition depended on the type of land-use change. Elevation, distance from the county government, and population were the main driving factors of land-use change. Road density, distance from the river, distance from the town/township government, and gross domestic product also affected land-use change to a certain extent, whereas relief and slope had less impact.

Whole-brain propagating patterns in human resting-state brain activities.

Repetitive propagating activities in resting-state brain activities have been widely observed in various species and regions. Because they resemble the preceding brain activities during tasks, they are assumed to reflect past experiences embedded in neuronal circuits. "Whole-brain" propagating activities may also reflect a process that integrates information distributed over the entire brain, such as visual and motor information. Here we reveal whole-brain propagating activities from human resting-state magnetoencephalography (MEG) and electroencephalography (EEG) data. We simultaneously recorded the MEGs and EEGs and estimated the source currents from both measurements. Then using our recently proposed algorithm, we extracted repetitive spatiotemporal patterns from the source currents. The estimated patterns consisted of multiple frequency components, each of which transiently exhibited the frequency-specific resting-state networks (RSNs) of functional MRIs (fMRIs), such as the default mode and sensorimotor networks. A simulation test suggested that the spatiotemporal patterns reflected the phase alignment of the multiple frequency oscillators induced by the propagating activities along the anatomical connectivity. These results argue that whole-brain propagating activities transiently exhibited multiple RSNs in their multiple frequency components, suggesting that they reflected a process to integrate the information distributed over the frequencies and networks.

Spatiotemporal heterogeneity and its determinants of COVID-19 transmission in typical labor export provinces of China.

BACKGROUND: Previous studies have indicated that the risk of infectious disease spread is greatest in locations where a population has massive and convenient access to the epicenter of an outbreak. However, the spatiotemporal variations and risk determinants of COVID-19 in typical labor export regions of China remain unclear. Understanding the geographical distribution of the disease and the socio-economic factors affecting its transmission is critical for disease prevention and control. METHODS: A total of 2152 COVID-19 cases were reported from January 21 to February 24, 2020 across the 34 cities in Henan and Anhui. A Bayesian spatiotemporal hierarchy model was used to detect the spatiotemporal variations of the risk posed by COVID-19, and the GeoDetector q statistic was used to evaluate the determinant power of the potential influence factors. RESULTS: The risk posed by COVID-19 showed geographical spatiotemporal heterogeneity. Temporally, there was an outbreak period and control period. Spatially, there were high-risk regions and low-risk regions. The high-risk regions were mainly in the southwest areas adjacent to Hubei and cities that served as economic and traffic hubs, while the low-risk regions were mainly in western Henan and eastern Anhui, far away from the epicenter. The accessibility, local economic conditions, and medical infrastructure of Wuhan in Hubei province all played an important role in the spatiotemporal heterogeneity of COVID-19 transmission. The results indicated that the q statistics of the per capita GDP and the proportion of primary industry GDP were 0.47 and 0.47, respectively. The q statistic of the population flow from Wuhan was 0.33. In particular, the results showed that the q statistics for the interaction effects between population density and urbanization, population flow from Wuhan, per capita GDP, and the number of doctors were all greater than 0.8. CONCLUSIONS: COVID-19 showed significant spatiotemporal heterogeneity in the labor export regions of China. The high-risk regions were mainly located in areas adjacent to the epicenter as well as in big cities that served as traffic hubs. Population access to the epicenter, as well as local economic and medical conditions, played an important role in the interactive effects of the disease transmission.

Can deep learning beat numerical weather prediction?

The recent hype about artificial intelligence has sparked renewed interest in applying the successful deep learning (DL) methods for image recognition, speech recognition, robotics, strategic games and other application areas to the field of meteorology. There is some evidence that better weather forecasts can be produced by introducing big data mining and neural networks into the weather prediction workflow. Here, we discuss the question of whether it is possible to completely replace the current numerical weather models and data assimilation systems with DL approaches. This discussion entails a review of state-of-the-art machine learning concepts and their applicability to weather data with its pertinent statistical properties. We think that it is not inconceivable that numerical weather models may one day become obsolete, but a number of fundamental breakthroughs are needed before this goal comes into reach. This article is part of the theme issue 'Machine learning for weather and climate modelling'.

Emission characteristics and control scenario analysis of VOCs from heavy-duty diesel trucks.

Vehicle exhaust substantially contributes to ambient volatile organic compounds (VOCs) that imperil environmental and human health. The quantitative characterization of VOCs derived from heavy-duty diesel trucks (HDDTs) at a high spatiotemporal resolution is an important prerequisite of atmospheric quality management. However, there is little knowledge about VOC emission characteristics and accurate control policies of HDDTs owing to limited fine-grained traffic activity data. To fill this gap, this research aims to construct a link-level and hourly-based VOC emission inventory of HDDTs by combining fine-grained trajectory data, detailed vehicle specification information, localized emission factors, and underlying geographic information. The emission reduction potentials of different emission control scenarios were also evaluated. The research was conducted in Hebei Province, a predominant heavy industrial province in China. The results demonstrated that HDDTs with China 3 and below emission standards contributed to 74.85% of the HDDT generated VOC emissions, although they only accounted for 25.43% of the HDDTs operating on the road networks. The VOC emission characteristics of HDDTs were further explored at various temporal and spatial scales. Temporally, the difference between the maximum and minimum hourly VOC emissions reached 29.19%, and daily emission changes were considerably affected by holidays. Spatially, road segments with higher emission intensities and statistically significant emission hot spots were primarily distributed in intercity highways and national freeways, reflecting the contribution of high freight activity to the VOC emissions. Emission control scenario simulations demonstrated that improving HDDT emission standards can reduce VOC emissions by up to 80.06%. The results of this study contribute to a deeper understanding of the spatiotemporal patterns of VOC emissions from HDDTs and the effectiveness of emission reduction measures.

Spatiotemporal behaviors of the ridership of a public transportation system during an epidemic outbreak: case of MERS in Seoul.

During May and June 2015, an outbreak of the Middle East respiratory syndrome (MERS) occurred in Korea, which raised the fear of contagion throughout society and suppressed the use of public transportation systems. Exploring daily ridership data of the Seoul bus transportation system, along with the number of infected patients and search volume in web portals, we observe that ridership decreased abruptly while attention was heavily focused online. Then this temporal reduction recovered exponentially with a characteristic time of 3 weeks when newly confirmed cases began to decrease. We also find with the data of ranked keywords of web portals that areas with severely reduced ridership tended to cluster and spatiotemporal variations of such clusters were highly associated with general hospitals where MERS patients were treated. On the other hand, the spatial reduction in ridership relaxed algebraically with the distance from a general hospital while the outbreak was severe. We further probe the influence of the epidemic outbreak in the framework of linear response theory, which relates the responses to the epidemic outbreak ("perturbation") with correlations in the absence of the perturbation. Indeed, the spatial correlation function of the ridership changes is observed to follow a power law, sharing the same exponent as the spatial relaxation of the response function. This new theoretical approach offers a useful tool for understanding responses of public transportation system to epidemic or accidental disasters.

Network and cellular mechanisms underlying heterogeneous excitatory/inhibitory balanced states.

Recent work has explored spatiotemporal relationships between excitatory (E) and inhibitory (I) signaling within neural networks, and the effect of these relationships on network activity patterns. Data from these studies have indicated that excitation and inhibition are maintained at a similar level across long time periods and that excitatory and inhibitory currents may be tightly synchronized. Disruption of this balance-leading to an aberrant E/I ratio-is implicated in various brain pathologies. However, a thorough characterization of the relationship between E and I currents in experimental settings is largely impossible, due to their tight regulation at multiple cellular and network levels. Here, we use biophysical neural network models to investigate the emergence and properties of balanced states by heterogeneous mechanisms. Our results show that a network can homeostatically regulate the E/I ratio through interactions among multiple cellular and network factors, including average firing rates, synaptic weights and average neural depolarization levels in excitatory/inhibitory populations. Complex and competing interactions between firing rates and depolarization levels allow these factors to alternately dominate network dynamics in different synaptic weight regimes. This leads to the emergence of distinct mechanisms responsible for determining a balanced state and its dynamical correlate. Our analysis provides a comprehensive picture of how E/I ratio changes when manipulating specific network properties, and identifies the mechanisms regulating E/I balance. These results provide a framework to explain the diverse, and in some cases, contradictory experimental observations on the E/I state in different brain states and conditions.