RESUMEN

A growing number of studies have investigated how land surface temperature (LST) is influenced by a variety of driving factors; however, little effort has been made to identify the dominant ones. The suggested method used the Upper Awash Basin (UAB), Ethiopia, as an example to explore the spatial heterogeneity and factors affecting LST, which is critical for selecting effective mitigation strategies to manage the thermal environment. The study employed two models: ordinary least squares (OLS) and geographically weighted regression (GWR). The OLS model was first used to capture the overall relationship between LST and some biophysical factors. The GWR was then utilized to investigate the spatial non-stationary relationships between LST and its influencing biophysical factors. Although the method was tested in UAB, Ethiopia, it can be applied in similar agroecosystems, to identify the dominant factors that influence LST and develop site-specific LST mitigation strategies.•The OLS and GWR models investigated the spatial heterogeneities of the influencing factors and LST.•Biophysical parameters such as enhanced vegetation index (EVI), modified normalized difference water index (MNDWI), normalized difference built-up index (NDBI), normalized difference bareness index (NDBaI), albedo and elevation were used as potential driving environmental factors of LST•The models performance was computed using the adjusted coefficient of determination (adj. R2), Akaike Information Criterion (AICc), and residual sum of squares (RSS).

RESUMEN

In survey sampling, auxiliary information is used to precisely estimate the finite population parameters. There are several approaches available in the literature that provide a practical method for incorporating auxiliary information during the estimation stage. In order to effectively utilize the auxiliary information, a geographically weighted regression (GWR) model-assisted integrated estimator of finite population total under a two-phase sampling design has been proposed in this article. Spatial simulation studies have been conducted to empirically assess the statistical properties of the proposed estimator. In the presence of spatial non-stationarity, empirical findings reveal that the proposed estimator outperforms all existing estimators such as two-phase HT, ratio, and regression estimators, demonstrating the importance of spatial information in survey sampling.

RESUMEN

Wind velocity is usually assumed to obey a stationary stochastic process in wind engineering, and this may cause significant bias in describing extremely severe strong wind such as typhoons and thunderstorms. To take into account the non-stationary characteristics of extreme wind, a novel evolutionary power spectral density (EPSD) model is proposed, and the spectral representation method (SRM) is introduced to simulate the whole process of strong winds. Firstly, the wavelet transform (WT) method is adopted to capture the three-dimensional time-varying properties of the low-frequency mean winds, and the associated turbulence features, including turbulent intensity, gust factor, probability density function, and power spectrum, are analyzed in depth. Secondly, the measured horizontal EPSD of strong winds are estimated. Thirdly, the performance of the proposed EPSD model is validated. Finally, the whole process of non-stationary strong winds are simulated and discussed. The results show that the proposed EPSD models are in good agreement with the measured EPSD, and the time-frequency features of the power spectrum of the simulated winds are well reproduced, which provides a powerful tool for large eddy simulation and wind engineering studies under non-stationary extreme wind climate.

RESUMEN

We propose a multivariate GARCH model for non-stationary health time series by modifying the observation-level variance of the standard state space model. The proposed model provides an intuitive and novel way of dealing with heteroskedastic data using the conditional nature of state-space models. We follow the Bayesian paradigm to perform the inference procedure. In particular, we use Markov chain Monte Carlo methods to obtain samples from the resultant posterior distribution. We use the forward filtering backward sampling algorithm to efficiently obtain samples from the posterior distribution of the latent state. The proposed model also handles missing data in a fully Bayesian fashion. We validate our model on synthetic data and analyze a data set obtained from an intensive care unit in a Montreal hospital and the MIMIC dataset. We further show that our proposed models offer better performance, in terms of WAIC than standard state space models. The proposed model provides a new way to model multivariate heteroskedastic non-stationary time series data. Model comparison can then be easily performed using the WAIC.

Asunto(s)

RESUMEN

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety.

Asunto(s)

RESUMEN

Excessive urbanization leads to considerable nature deficiency and abundant artificial infrastructure in urban areas, which triggered intensive discussions on people's exposure to green space and outdoor artificial light at night (ALAN). Recent academic progress highlights that people's exposure to green space and outdoor ALAN may be confounders of each other but lacks systematic investigations. This study investigates the associations between people's exposure to green space and outdoor ALAN by adopting the three most used research paradigms: population-level residence-based, individual-level residence-based, and individual-level mobility-oriented paradigms. We employed the green space and outdoor ALAN data of 291 Tertiary Planning Units in Hong Kong for population-level analysis. We also used data from 940 participants in six representative communities for individual-level analyses. Hong Kong green space and outdoor ALAN were derived from high-resolution remote sensing data. The total exposures were derived using the spatiotemporally weighted approaches. Our results confirm that the negative associations between people's exposure to green space and outdoor ALAN are universal across different research paradigms, spatially non-stationary, and consistent among different socio-demographic groups. We also observed that mobility-oriented measures may lead to stronger negative associations than residence-based measures by mitigating the contextual errors of residence-based measures. Our results highlight the potential confounding associations between people's exposure to green space and outdoor ALAN, and we strongly recommend relevant studies to consider both of them in modeling people's health outcomes, especially for those health outcomes impacted by the co-exposure to them.

RESUMEN

This study examines the impact of austerity measures on mortality rates across Italian regions from 2003 to 2018. Since 2007, regions experiencing substantial healthcare financial deficits have been required to implement recovery plans (RPs). We use a recent difference-in-differences staggered matching estimator to assess the effects of this austerity policy on municipal-level monthly mortality rates. This allows us to evaluate the policy's spatial heterogeneity across treated municipalities, accounting for their distance from the nearest hospital. The analysis reveals a significant negative impact of austerity measures on health, particularly in peripheral areas and among vulnerable populations. Mortality rates are higher in regions under RPs, with this effect escalating with increasing distance from hospitals. The policy's impact is also more pronounced among vulnerable populations, with differences observed between genders and across seasons.

Asunto(s)

RESUMEN

Cost limitations often lead to the adoption of lower precision grids for soil sampling in large-scale areas, potentially causing deviations in the observed trace metal (TM) concentrations from their true values. Therefore, in this study, an enhanced Health Risk Assessment (HRA) model was developed by combining Monte Carlo simulation (MCS) and Empirical Bayesian kriging (EBK), aiming to improve the accuracy of health risk assessment under low-precision sampling conditions. The results showed that the increased sampling scale led to an overestimation of the non-carcinogenic risk for children, resulting in potential risks (the maximum Hazard index value was 1.08 and 1.64 at the 500 and 1000 m sampling scales, respectively). EBK model was suitable for predicting soil TM concentrations at large sampling scale, and the predicted concentrations were closer to the actual value. Furthermore, we found that the improved HRA model by combining EBK and MCS effectively reduced the possibility of over- or under-estimation of risk levels due to the increasing sampling size, and enhanced the accuracy and robustness of risk assessment. This study provides an important methodology support for health risk assessment of soil TMs under data limitation.

RESUMEN

Objective.The fact that ramp incremental exercise yields quasi-linear responses for pulmonary oxygen uptake (V˙O2) and heart rate (HR) seems contradictory to the well-known non-linear behavior of underlying physiological processes. Prior research highlights this issue and demonstrates how a balancing of system gain and response time parameters causes linearV˙O2responses during ramp tests. This study builds upon this knowledge and extracts the time-varying dynamics directly from HR andV˙O2data of single ramp incremental running tests.Approach.A large-scale open access dataset of 735 ramp incremental running tests is analyzed. The dynamics are obtained by means of 1st order autoregressive and exogenous models with time-variant parameters. This allows for the estimates of time constant (τ) and steady state gain (SSG) to vary with work rate.Main results.As the work rate increases,τ-values increase on average from 38 to 132 s for HR, and from 27 to 35 s forV˙O2. Both increases are statistically significant (p< 0.01). Further, SSG-values decrease on average from 14 to 9 bpm (km·h-1)-1for HR, and from 218 to 144 ml·min-1forV˙O2(p< 0.01 for decrease parameters of HR andV˙O2). The results of this modeling approach are line with literature reporting on cardiorespiratory dynamics obtained using standard procedures.Significance.We show that time-variant modeling is able to determine the time-varying dynamics HR andV˙O2responses to ramp incremental running directly from individual tests. The proposed method allows for gaining insights into the cardiorespiratory response characteristics when no repeated measurements are available.

Asunto(s)

RESUMEN

The frequent occurrence of extreme heat events has notably affected human's living environment, and a considerable number of studies have reported that green space is an efficient measure by investigating the correlation between green space and land surface temperature (LST). However, spatiotemporal effects of green space on LST still remain unclear. In this study, green space patterns (e.g., core, islet, perforation, edge, loop, bridge, and branch) were identified through morphological spatial pattern analysis (MSPA). Moreover, the effects of green space pattern on LST in three periods were investigated through three kinds of models. As indicated by the results: (1) the geographically and temporally weighted regression model exhibited the optimal performance compared with other two models. (2) in general, the core, the edge, the bridge, and the branch significantly contributed to cooling, and the islet hindered cooling. However, the perforation and the loop exerted significant dual nature effects with the similar quantity of the negative and positive coefficients, showing relatively complex impact mechanism. (3) the intensity of the effect of the respective MSPA class varied across the study area. The core had the most substantial effect, which distributed in the south and middle corners. (4) the result suggested that a neighborhood scale in China, which was 960 m in this study, served as a basic unit in green space management. The spatiotemporal non-stationarity of the effects of green space morphological patterns on LST provided important insights into urban thermal environment improvement through urban green space planning and design.

RESUMEN

Romantic relationships are defined by emotion dynamics, or how the emotions of one partner at a single timepoint can affect their own emotions and the emotions of their partner at the next timepoint. Previous research has shown that the level of these emotion dynamics plays a role in determining the state and quality of the relationship. However, this research has not examined whether the estimated emotion dynamics change over time, and how the change in these dynamics might relate to relationship outcomes, despite changes in dynamics being likely to occur. We examined whether the magnitude of variation in emotion dynamics over time was associated with relationship outcomes in a sample of 148 couples. Time-varying vector autoregressive models were used to estimate the emotion dynamics for each couple, and the average and standard deviation of the dynamics over time was related to relationship quality and relationship dissolution 1-2 years later. Our results demonstrate that certain autoregressive and cross-lagged parameters do show significant variation over time, and that this variation is associated with relationship outcomes. Overall, this study demonstrates the importance of accounting for change in emotion dynamics over time, and the relevance of this change to the prediction of future outcomes.

RESUMEN

Accelerating the attainment of carbon balance in Chinese cities has become pivotal in addressing global climate change and promoting green, low-carbon development. This study, encompassing 277 prefecture-level and above cities from 2007 to 2020, reveals a positive overall trend in China's urban carbon balance index. The evolution unfolds in two stages, demonstrating a distinct "tiered development" pattern across the eastern, central and western regions. Moreover, significant spatial agglomeration characteristics characterize China's carbon balance hot and cold spots throughout the study period, with their spatial agglomeration degree remaining stable. The standard deviation ellipse analysis confirms these hot and cold spots' alignment with China's economic development level and population distribution. The GTWR test results highlight the pronounced non-stationary characteristics of different driving factors in space and time, exhibiting variations in strength and direction among regions. Consequently, enhancing China's urban carbon balance requires tailored measures based on different areas' unique conditions and development characteristics, emphasizing a hierarchical and classified approach to leverage distinct driving factors and foster a green development system in China.

Asunto(s)

RESUMEN

The novel circumstance-driven bivariate integer-valued autoregressive (CuBINAR) model for non-stationary count time series is proposed. The non-stationarity of the bivariate count process is defined by a joint categorical sequence, which expresses the current state of the process. Additional cross-dependence can be generated via cross-dependent innovations. The model can also be equipped with a marginal bivariate Poisson distribution to make it suitable for low-count time series. Important stochastic properties of the new model are derived. The Yule-Walker and conditional maximum likelihood method are adopted to estimate the unknown parameters. The consistency of these estimators is established, and their finite-sample performance is investigated by a simulation study. The scope and application of the model are illustrated by a real-world data example on sales counts, where a soap product in different stores with a common circumstance factor is investigated.

RESUMEN

The aim of this study was to quantify the effect of land use change (LUC) implemented to meet nutrient load targets for a freshwater lake in New Zealand. We used the Soil and Water Assessment Tool (SWAT) model in combination with a non-parametric statistical test to determine whether afforestation of 15% of a subcatchment area was adequate to meet assigned nutrient load targets. A regional management authority set nutrient load targets of reduction in total nitrogen (TN) by 0.9 t yr-1 and reduction in total phosphorus (TP) by 0.05 t yr-1 to avoid eutrophication in the receiving waters of a freshwater lake. The load reduction was designed to be achieved through 200 ha of LUC from pasture to trees. Analysis of nutrient loads before, during, and following LUC shows that a 15% increase in forest cover decreased the annual flow (7.2%), TP load (33.3%), and TN load (13.1%). As flow and water quality observations were discrete and at irregular intervals, we used a parametric test and the SWAT model as different lines of evidence to demonstrate the effect of afforestation on flow and water quality. Policymakers concerned with decisions about LUC to improve the quality of receiving waters can benefit from applying our findings and using a statistical and numerical modelling framework to evaluate the adequacy of land use change to support improvements in water quality.

Asunto(s)

RESUMEN

Background: Sub-Saharan Africa (SSA) has one of the highest prevalence of malnutrition among children under 5 in the world. It is also the region most vulnerable to the adverse effect of climate change, and the one that records the most armed conflicts. The chains of causality suggested in the literature on the relationship between climate change, armed conflict, and malnutrition have rarely been supported by empirical evidence for SSA countries. Methods: This study proposes to highlight, under the hypothesis of spatial non-stationarity, the influence of climatic variations and armed conflicts on malnutrition in children under 5 in Ethiopia, Kenya, and Nigeria. To do this, we use spatial analysis on data from Demographic and Health Surveys (DHS), Uppsala Conflict Data Program Georeferenced Event Dataset (UCDP GED), Climate Hazards center InfraRed Precipitation with Station data (CHIRPS) and Moderate Resolution Imaging Spectroradiometer (MODIS). Results: The results show that there is a spatial autocorrelation of malnutrition measured by the prevalence of underweight children in the three countries. Also, local geographically weighted analysis shows that armed conflict, temperature and rainfall are positively associated with the prevalence of underweight children in localities of Somali in Ethiopia, Mandera and Turkana of Wajir in Kenya, Borno and Yobe in Nigeria. Conclusion: In conclusion, the results of our spatial analysis support the implementation of conflict-sensitive climate change adaptation strategies.

RESUMEN

Capturing asymmetry among time series is an important area of research as it provides a range of information regarding the behaviour and distribution of the underlying series, which in turn proves to be useful for prediction. Classically, this can be achieved by modeling the skewness of the underlying series, usually using the standard measure. We present here an improved measure of skewness for time series which are integrated by a certain order, which is easy to calculate and proves to be advantageous over the existing one. We complement our methodology by implementing it to represent the heavy asymmetry among the daily COVID-19 case counts of several countries.•Improved skewness measure proves to be better than the usual skewness measure for time series data•This new measure is applied on COVID-19 daily counts to capture the asymmetry appropriately.

RESUMEN

False memory leads to inaccurate decisions and unnecessary challenges. Researchers have conventionally used electroencephalography (EEG) to study false memory under different emotional states. However, EEG non-stationarity has scarcely been investigated. To address this problem, this study utilized the nonlinear method of recursive quantitative analysis to analyze the non-stationarity of EEG signals. Deese-Roediger-McDermott paradigm experiments were used to induce false memory wherein semantic words were highly correlated. The EEG signals of 48 participants with false memory associated with different emotional states were collected. Recurrence rate (RR), determination rate (DET), and entropy recurrence (ENTR) data were generated to characterize EEG non-stationarity. Behavioral outcomes exhibited significantly higher false-memory rates in the positive group than in the negative group. The prefrontal, temporal, and parietal regions yielded significantly higher RR, DET, and ENTR values than other brain regions in the positive group. However, only the prefrontal region had significantly higher values than other brain regions in the negative group. Therefore, positive emotions enhance non-stationarity in brain regions associated with semantics compared with negative emotions, leading to a higher false-memory rate. This suggests that non-stationary alterations in brain regions under different emotional states are correlated with false memory.

Asunto(s)

RESUMEN

Resting-state magnetoencephalography (MEG) data show complex but structured spatiotemporal patterns. However, the neurophysiological basis of these signal patterns is not fully known and the underlying signal sources are mixed in MEG measurements. Here, we developed a method based on the nonlinear independent component analysis (ICA), a generative model trainable with unsupervised learning, to learn representations from resting-state MEG data. After being trained with a large dataset from the Cam-CAN repository, the model has learned to represent and generate patterns of spontaneous cortical activity using latent nonlinear components, which reflects principal cortical patterns with specific spectral modes. When applied to the downstream classification task of audio-visual MEG, the nonlinear ICA model achieves competitive performance with deep neural networks despite limited access to labels. We further validate the generalizability of the model across different datasets by applying it to an independent neurofeedback dataset for decoding the subject's attentional states, providing a real-time feature extraction and decoding mindfulness and thought-inducing tasks with an accuracy of around 70% at the individual level, which is much higher than obtained by linear ICA or other baseline methods. Our results demonstrate that nonlinear ICA is a valuable addition to existing tools, particularly suited for unsupervised representation learning of spontaneous MEG activity which can then be applied to specific goals or tasks when labelled data are scarce.

Asunto(s)

RESUMEN

Environmental regulation is expected to stimulate green innovation for the promotion of urban sustainability, while the effectiveness of this stimulus has long been debated under the Porter hypothesis and the crowding out theory. Empirical studies under different contexts have not reached a consistent conclusion yet. Based on the data of 276 cities in China from 2003 to 2013, this study captures the spatiotemporal non-stationarity in the effects of environmental regulation on green innovation with the combination of Geographically and Temporally Weighted Regression (GTWR) and Dynamic Time Warping (DTW) algorithm. The results show that environmental regulation has an overall U-shape impact on green innovation, indicating that the Porter hypothesis and the crowding out theory are not in conflict, but are theoretical interpretations of different stages of local responses to environmental regulation. Specifically, the effects of environmental regulation on green innovation present to be diverse in patterns that include enhancing, stagnant, undermining, U-shape, and inverted U-shape. These contextualized relationships are shaped by local industrial incentives and innovation capacities of pursing green transformations. The spatiotemporal findings allow policymakers to better understand the multi-staged and geographically diverse impacts of environmental regulation on green innovations, and formulate targeted policies for different localities.

Asunto(s)

RESUMEN

Although quantitative environmental (in)justice research demonstrates a disproportionate burden of toxic chemical hazard risks among racial/ethnic minorities and people in low socioeconomic positions, limited knowledge exists on how racial/ethnic and socioeconomic groups across geographic spaces experience toxic chemical hazards. This study analyzed the spatial non-stationarity in the associations between toxic chemical hazard risk and community characteristics of census block groups in Texas, USA, for 2017 using a multiscale geographically weighted regression. The results showed that the percentage of Black or Asian population has significant positive associations with toxic risk across block groups in Texas, meaning that racial minorities suffered more from toxic risk wherever they are located in the state. By contrast, the percentage of Hispanic or Latino has a positive relationship with toxic risk, and the relationship varies locally and is only significant in eastern areas of Texas. Statistical associations between toxic risk and socioeconomic variables are not stationary across the state, showing sub-state patterns of spatial variation in terms of the sign, significant level, and magnitude of the coefficient. Income has a significant negative association with toxic risk around the Dallas-Fort Worth-Arlington Metropolitan Statistical Area. Proportions of people without high school diploma and the unemployment rate both have positive relationships with toxic risk in the eastern area of Texas. Our findings highlight the importance of identifying the spatial patterns of the association between toxic chemical hazard risks and community characteristics at the census block group level for addressing environmental inequality.

Asunto(s)