High Autocorrelation Research Articles

A multifaceted analysis of decreasing trust in health institutions in the EU during the COVID-19 pandemic

BackgroundPublic trust is crucial during health crises, such as the COVID-19 pandemic, for the effective implementation of scientific and policy interventions. This study investigates factors contributing to the decline in trust in the health sector within the European Union during the pandemic.MethodsUsing the publicly available 2020–2022 Standard Eurobarometer dataset, we employed a "geography of trust" approach. We applied equal quantile mapping to visualise high and low trust countries and Local Spatial Autocorrelation (LISA) to identify statistically significant clusters. Using a series of one-way ANOVA, we found associations between socio-economic and other variables available in the same dataset with trust.ResultsTrust in health institutions and medical staff declined in 20 of 27 EU countries from 2020 to 2022. Clear geographical clusters of Western and Central Europe as the high-trust countries and Eastern Europe as the low-trust countries emerged through mapping. One-way ANOVA revealed that contrary to several studies, age, gender, and most demographics did not vary with trust, but trust was directly associated with the satisfaction with measures taken to fight the COVID-19 pandemic, and individual perceptions about their life and national affairs (p < 0.001).ConclusionHigh-trust Western European countries share characteristics in health systems, governance, and pandemic responses. Understanding this interplay could inform trust-building strategies. The persistent low trust in Eastern Europe may stem from slow government responses and requires nuanced approaches considering unique contexts. The decline in traditionally high-trust countries highlights challenges even in established social contexts. This study provides valuable insights for policymakers aiming to foster trust during health crises.

Algorithm for Designing Waveforms Similar to Linear Frequency Modulation Using Polyphase-Coded Frequency Modulation

Linear frequency modulation (LFM) waveforms have been widely adopted due to their excellent performance characteristics, such as good Doppler tolerance and ease of physical implementation. However, LFM waveforms suffer from high autocorrelation sidelobes (ACSLs) and limited design flexibility. Phase-coded frequency modulation (PCFM) waveforms can be used to design waveforms similar to LFM, offering greater design flexibility to optimize ACSLs. However, it has been found that the initial PCFM waveform experiences spectral expansion during the ACSL optimization process, which reduces its similarity to LFM. Therefore, this article jointly optimizes the ACSLs and spectrum of the initial PCFM waveform, establishes an optimized mathematical model, and then solves it using the heavy-ball gradient descent algorithm. Numerical experiments indicate that the proposed method effectively addresses the problem of waveform similarity degradation caused by spectral expansion while reducing waveform ACSLs. At the same time, a balance between reducing waveform ACSLs and preserving waveform similarity can be achieved by adjusting the parameters.

Wave-Former: Lag removing univariate long time series forecasting transformer for ocean waves

Long-Sequence Time Series Forecasting (LSTSF) of ocean waves using univariate modelling has always been a challenging task in the field of oceanography as it demands a high prediction capacity model capable to capture precise long-range dependency between input and output variables. Inherently, it exhibits the problem of “time lag or phase shift” in predictions which is due to high autocorrelation in between time dependent sequential values. Very few researchers had tried to remove this “phase shift” problem in the univariate modelling including the author of present work wherein preprocessing of time series data was done by different methods. Many failed in gaining high prediction accuracy at longer forecasting intervals. Overcoming these limitations, present work aims for LSTSF of ocean waves ranging from 24 to 144 hours (hrs) at the three locations of Gulf of Mexico using “WaveFormer” architecture based on the Informer. Probabilistic self-attention mechanism has proved to be efficient to remove the “time lag or phase shift” in ocean waves as well as in predicting the waves at large time intervals (144 hrs). Low Root Mean Squared Errors (RMSEs) (0.045–0.100) and high correlation coefficients (0.90–0.99), low values of test loss (0.002–0.012) of all the developed models demonstrated the competency of WaveFormer.

Coupling is key for the tropical Indian and Atlantic oceans to boost super El Niño.

The influences of the tropical Indian and Atlantic oceans on the development of El Niño Southern Oscillation (ENSO), especially regarding super El Niño events, have been a subject of debate. In particular, several studies argue that these cross-basin influences may be mere statistical artifacts resulting from the high auto-correlation of ENSO. To clarify this issue, we conduct a series of perfect model hindcast experiments to untangle the individual and synergistic effects of the tropical Indian and Atlantic oceans. Our results clearly demonstrate that without these cross-basin effects, the Pacific warming would rarely reach super El Niño level. Specifically, the individual effect of the Indian Ocean efficiently enhances the development of super El Niño events. In contrast, the Atlantic's effect is initially limited because it fails to establish the Bjerknes feedback in the Pacific. However, when coupled with the Indian Ocean, the Atlantic's effect becomes more pronounced as it is amplified by the Bjerknes feedback established by the Indian Ocean.

Microtopographic response of tilled loess slopes during stages of water erosion development

The tilled slopes in the loess region are characterized by artificial management practices that result in variations in microtopography, which serve as both the site of water erosion and a significant factor contributing to further slope erosion development. However, the response of microtopography during the process of erosion development on tilled loess slopes is unclear. In this study, artificial rainfall simulations were conducted to simulate the development of water erosion (splash erosion (SpE), sheet erosion (ShE), and rill erosion (RE)) under three slope gradients (5°, 10°, and 20°) and three tillage practices (artificial backhoe (AB), artificial digging (AD), and contour tillage (CT)) at a rainfall intensity of 60 mm h−1. The spatial correlation and heterogeneity of the microtopography were analysed using semivariogram functions and fractal dimensions. The response characteristics of microtopography during the water erosion development process on tilled loess slopes were analysed from three perspectives: microtopography factors, erosion equilibrium areas, and slope strips M-ΔDEM. The results indicated that the microtopography process data exhibited moderate to high spatial autocorrelation. There is a positive correlation between microtopographic factors and the slope gradient. Tilled slopes exhibit erosion equilibrium effects during the processes of splash erosion, sheet erosion, and rill erosion. These equilibrium effects are located in the longitudinal ranges of 700 to 1200 mm, 700 to 1300 mm, and 800 to 1100 mm, respectively. Soil erosion and deposition primarily occur in the middle and lower parts of the slope, with 70 % of the slope’s elevation changing within 10 mm, while the remaining changes can reach 25 mm. Among the three tillage practices, AB is the most susceptible to slope influences, which can lead to soil loss, and an optimal slope gradient exists for implementing AB measures. In the case of CT at a slope of 20°, ridge breaking is more likely to occur, but considering the combined effects of soil loss suppression and the difficulty in forming rills, it is recommended to prioritize CT as the preferred tillage practice among the three practices. This study can provide a theoretical foundation for understanding the mechanisms through which microtopography influences water erosion. Additionally, it can serve as a theoretical basis for calibrating parameters in soil erosion models for sloping cultivated lands.

Analysis of Spatio-Temporal Changes and Driving Factors of Wetland Ecosystem Health Based on the AHP-SOM-DPSR Model—A Case Study of Wetlands in the Qin-Mang River

Wetland ecosystems are vital for ecological security. However, rapid industrialization and urbanization have led to regional degradation, particularly in areas like the Qin-mang River Basin, a pivotal site in China’s ecological security strategy. Urgent measures are required to safeguard the health of its wetland ecosystems. This study employs the hierarchical analysis method-neural network-driver-pressure-state-response (AHP-SOM-DPSR) model to evaluate the ecological health of wetland ecosystems in the Qin-mang River Basin over a thirty-year period, from 1992 to 2022. Initially, the Drive-Pressure-State-Response (DPSR) model establishes a comprehensive indicator system encompassing 19 indicators spanning driving forces, pressures, states, and responses. Subsequently, a hybrid approach combining Analytic Hierarchy Process (AHP) and Self-Organizing Map (SOM) determines indicator weights to evaluate spatiotemporal ecosystem changes over the past three decades. Additionally, spatial autocorrelation theory analyzes ecosystem health in the study area. Finally, Pearson correlation coefficient analysis examines the driving factors influencing ecosystem health and their impacts. Results indicate: (1) Ecosystem health has deteriorated gradually from 1992 to 2022, underscoring the imperative for enhanced wetland management in the Qin-mang River Basin. (2) High spatial autocorrelation areas, primarily in the central-southern and northern regions, highlight priority zones for wetland ecosystem management. (3) Urbanization levels, average temperature, and total population significantly impact wetland ecosystem health in the Qin-mang River Basin. These findings offer valuable scientific insights for guiding ecological management and conservation efforts in the region.

Random forest and spatial cross-validation performance in predicting species abundance distributions

Random forests (RF) have been widely used to predict spatial variables. Several studies have shown that spatial cross-validation (CV) methods consistently cause RF to yield larger prediction errors compared to standard CV methods. This study examined the impact of species characteristics and data features on the performance of the standard RF and spatial CV approaches for predicting species abundance distribution. It compared the standard 5-fold CV, design-based validation, and three different spatial CV methods, such as spatial buffering, environmental blocking, and spatial blocking. Validation samples were randomly selected for design-based validation without replacement. We evaluated their predictive performance (accuracy and discrimination metrics) using artificial species abundance data generated by a linear function of a constant term (β0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\beta _0$$\\end{document}) and a random error term following a zero-mean Gaussian process with a covariance matrix determined by an exponential correlation function. The model was tuned over multiple simulations to consider different mean levels of species abundance, spatial autocorrelation variation, and species detection probability. Here we found that the standard RF had poor predictive performance when spatial autocorrelation was high and the species probability of detection was low. Design-based validation and standard K-fold CV were found to be the most effective strategies for evaluating RF performance compared to spatial CV methods, even in the presence of high spatial autocorrelation and imperfect detection for random samples. For weakly or moderately clustered samples, they yielded good modelling efficiency but overestimated RF’s predictive power, while they overestimated modelling efficiency, predictive power, and accuracy for strongly clustered samples with high spatial autocorrelation. Globally, the checkerboard pattern in the allocation of blocks to folds in blocked spatial CV was found to be the most effective CV approach for clustered samples, whatever the degree of clustering, spatial autocorrelation, or species abundance class. The checkerboard pattern in spatial CV was found to be the best method for random or systematic samples with spatial autocorrelation, but less effective than non-spatial CV approaches. Failing to take data features into account when validating models can lead to unrealistic predictions of species abundance and related parameters and, therefore, incorrect interpretations of patterns and conclusions. Further research should explore the benefits of using blocked spatial K-fold CV with checkerboard assignment of blocks to folds for clustered samples with high spatial autocorrelation.

Spatial and temporal forecasting of groundwater anomalies in complex aquifer undergoing climate and land use change

Monitoring groundwater (GW) level variations, or anomalies in multiple wells, over long periods of time is essential to understanding changes in regional groundwater resource availability. However, it is challenging to predict these GW anomalies over the long term in agricultural areas due to complicated boundary conditions, heterogeneous hydrogeological characteristics, and groundwater extraction, as well as nonlinear interactions among these factors. To overcome this challenge, we developed an advanced modeling framework based on a recurrent neural network of long short-term memory (LSTM) as an alternative to complex and computationally expensive physical models. GW anomalies were forecast two months in advance (t + 2) based on the evaluation of drivers that influence GW dynamics in densely irrigated agricultural regions. An application and evaluation of this new approach was conducted in the Wisconsin Central Sands (WCS) region in the U.S., one of the most productive agricultural regions. The modeling framework was developed for the period 1958–2020 by utilizing easily accessible dynamic and static variables to represent hydrometeorological and geological characteristics. GW anomaly observations were acquired from 26 piezometers (wells) installed in the sandy aquifer in WCS over 10–60 years. The subset of GW observations from ∼ 10–60 years, not used in model training, can forecast GW anomalies two months out with a coefficient of determination R2 ∼ 0.8. Additionally, MAE was less than 0.34 m/month across the study region for both temporal and spatial modeling frameworks. Groundwater anomalies showed high spatiotemporal variability, and their responses are influenced differently by boundary conditions, catchment geology, climate, and topography across locations. Sites with higher autocorrelation with previous two-months GW anomalies reduced bias by increasing R2. Land use change and irrigation pumping have interactive effects on GW anomalies forecasting. The novelty of this study is identifying the regional drivers of GW fluxes. This case-specific information and location-related simplification, modification, and assumption of LSTM is a unique contribution to the existing literature. Our framework can be used as an alternative method of simulating water availability and groundwater level changes in areas where subsurface properties are unknown or difficult to determine.

Spatial Distribution Characteristics and Influencing Factors of Intangible Cultural Heritage in the Yunnan, Guangxi, and Guizhou Rocky Desertification Area

The Yunnan, Guangxi, and Guizhou rocky desertification area is characterized by a fragile ecological environment, along with the inhabitation of many ethnic minorities, making it the region with the most poverty-alleviation targets among the 14 concentrated poverty-stricken areas in China. However, this area has obvious cultural resources, but it lacks systematic study. Therefore, integral investigation and research which can promote the sustainable development with abundant culture resource should pay more attention to it. This study, with 1113 national and provincial intangible cultural heritage projects in this area as research objects, used ArcGIS to visualize their geographic data for research. Geodetector was used to quantify the influence of relevant factors and analyze the distribution characteristics and influencing factors of intangible cultural heritage in this region. By providing a correlative strategy, the aim is to promote the coordinated development of the regional cultural economy. The results are as follows: (1) Guizhou exhibits the highest distribution of intangible cultural heritage projects, followed by Guangxi and Yunnan, indicating an uneven number of intangible cultural heritage projects. (2) A spatial analysis shows the clustered spatial distribution of intangible cultural heritage projects in this area, forming a high-density core area and multiple sub high-density areas with high spatial autocorrelation. (3) The spatial distribution of intangible cultural heritage in the rocky desertification areas of Yunnan, Guangxi, and Guizhou is generally correlated with survival orientation and natural environmental factors and positively correlated with development orientation and social, economic, ethnic, and cultural factors. Among many factors, cultural environment quantity exhibits the strongest correlation with the spatial distribution of intangible cultural heritage. (4) Based on the degree of correlation with different influencing factors, four development strategies are proposed: digitization of intangible cultural heritage inheritance and development, multi-media intangible cultural heritage creation and dissemination, innovative construction of intangible cultural heritage exhibitions, and tourism-driven dynamic inheritance of intangible cultural heritage.

Flicker and reading speed: Effects on individuals with visual sensitivity.

Flicker and patterns of stripes in the modern environment can evoke visual illusions, discomfort migraine, and seizures. We measured reading speed while striped and less striped texts were illuminated with LED lights. In Experiment 1, the lights flickered at 60 Hz and 120 Hz compared to 60 kHz (perceived as steady light). In Experiment 2, the lights flickered at 60 Hz or 600 Hz (at which frequency the phantom array is most visible), and were compared to continuous light. Two types of text were used: one containing words with high horizontal autocorrelation (striped) and another containing words with low autocorrelation (less striped). We measured the number of illusions participants saw in the Pattern Glare (PG) Test. Overall, reading speed was slowest during the 60 Hz and 600 Hz flicker and was slower when reading the high autocorrelation text. Interestingly, the low PG group showed greater effects of flicker on reading speed than the high PG group, which tended to be slower overall. In addition, reading speed in the high PG group was reduced when the autocorrelation of the text was high. These findings suggest that uncomfortable visual environments reduce reading efficiency, the more so in individuals who are visually sensitive.

Application of Portfolio Optimization to Achieve Persistent Time Series

The greater the persistence in a financial time series, the more predictable it becomes, allowing for the development of more effective investment strategies. Desirable attributes for financial portfolios include persistence, smoothness, long memory, and higher auto-correlation. We argue that these properties can be achieved by adjusting the composition weights of the portfolio. Considering the fractal nature of typical financial time series, the fractal dimension emerges as a natural metric to gauge the smoothness of the portfolio trajectory. Specifically, the Hurst exponent is designed for measuring the persistence of time series. In this paper, we introduce an optimization method inspired by the Hurst exponent and signal processing to mitigate the irregularities in the portfolio trajectory. We illustrate the effectiveness of this approach using real data from an S &P100 dataset.

A novel correction method for modelling parameter-driven autocorrelated time series with count outcome

BackgroundCount time series (e.g., daily deaths) are a very common type of data in environmental health research. The series is generally autocorrelated, while the widely used generalized linear model is based on the assumption of independent outcomes. None of the existing methods for modelling parameter-driven count time series can obtain consistent and reliable standard error of parameter estimates, causing potential inflation of type I error rate.MethodsWe proposed a new maximum significant ρ correction (MSRC) method that utilizes information of significant autocorrelation coefficient ρ estimate within 5 orders by moment estimation. A Monte Carlo simulation was conducted to evaluate and compare the finite sample performance of the MSRC and classical unbiased correction (UB-corrected) method. We demonstrated a real-data analysis for assessing the effect of drunk driving regulations on the incidence of road traffic injuries (RTIs) using MSRC in Shenzhen, China. Moreover, there is no previous paper assessing the time-varying intervention effect and considering autocorrelation based on daily data of RTIs.ResultsBoth methods had a small bias in the regression coefficients. The autocorrelation coefficient estimated by UB-corrected is slightly underestimated at high autocorrelation (≥ 0.6), leading to the inflation of the type I error rate. The new method well controlled the type I error rate when the sample size reached 340. Moreover, the power of MSRC increased with increasing sample size and effect size and decreasing nuisance parameters, and it approached UB-corrected when ρ was small (≤ 0.4), but became more reliable as autocorrelation increased further. The daily data of RTIs exhibited significant autocorrelation after controlling for potential confounding, and therefore the MSRC was preferable to the UB-corrected. The intervention contributed to a decrease in the incidence of RTIs by 8.34% (95% CI, -5.69–20.51%), 45.07% (95% CI, 25.86–59.30%) and 42.94% (95% CI, 9.56–64.00%) at 1, 3 and 5 years after the implementation of the intervention, respectively.ConclusionsThe proposed MSRC method provides a reliable and consistent approach for modelling parameter-driven time series with autocorrelated count data. It offers improved estimation compared to existing methods. The strict drunk driving regulations can reduce the risk of RTIs.

Mixing-layer-height-referenced ozone vertical distribution in the lower troposphere of Chinese megacities: stratification, classification, and meteorological and photochemical mechanisms

Abstract. Traditional tropospheric ozone (O3) climatology uses a simple average substantially smoothed stratification structure in individual O3 profiles, limiting our ability to properly describe and understand how O3 is vertically distributed at the interface between the mixing layer (ML) and free troposphere (FT). In this study, we collected 1897 ozonesonde profiles from two Chinese megacities (Beijing and Hong Kong) over the period 2000–2022 to investigate the climatological vertical heterogeneity of the lower-tropospheric O3 distribution with a mixing-layer-height-referenced (h-referenced) vertical coordinate system. The mixing-layer height (h) was first estimated following an integral method that integrates the information of temperature, humidity, and cloud. After that, a so-called h-referenced vertical distribution of O3 was determined by averaging all individual profiles expressed as a function of z/h rather than z (where z is altitude). We found that the vertical stratification of O3 is distributed heterogeneously in the lower troposphere, with stronger vertical gradients at the surface layer and ML–FT interface. There are low vertical autocorrelations of O3 between the ML and FT but high autocorrelations within each of the two atmospheric compartments. These results suggest that the ML–FT interface acts as a geophysical “barrier” separating air masses of distinct O3 loadings. This barrier effect varies with season and city, with an ML–FT detrainment barrier in summer (autumn) and an FT–ML entrainment barrier in other seasons in Beijing (Hong Kong). Based on a Student's t test, daily h-referenced O3 profiles were further classified into three typical patterns: MLO3-dominated, FTO3-dominated, and uniform distribution. Although the FTO3-dominated pattern occurs most frequently during the whole study period (69 % and 54 % of days in Beijing and Hong Kong, respectively), the MLO3-dominated pattern prevails in the photochemically active season, accounting for 47 % of summer days in Beijing and 54 % of autumn days in Hong Kong. These occurrences of the MLO3-dominated pattern are significantly more frequent than in previously reported results at northern mid-latitudes, indicating intensive photochemical MLO3 production under the high-emission background of a Chinese megacity. From a FTO3-dominated to MLO3-dominated pattern, the O3 precursor CH2O (NO2) experiences a substantial increase (decrease) in Beijing but a slight change in Hong Kong. Vertically, the increment of CH2O is larger in the upper ML, and the decrement of NO2 is larger in the lower ML. Such vertical changes in O3 precursors push O3 production sensitivity away from the VOC-limited regime and facilitate high-efficiency production of O3 via photochemical reactions, particularly in the upper ML of Beijing.

MIMO radar partially correlated waveform design based on chirp rate diversity

Waveform design is a significant topic in multiple-input-multiple-output (MIMO) radar. In this paper, we address the problem of waveform design for colocated narrowband MIMO radar transmit beampattern synthesis. The existing methods have not comprehensively considered the transmit beampattern synthesis under multiple practical constraints, such as constant modulus constraint, Doppler tolerance, high range resolution and low auto-correlation sidelobe. We consider these practical constraints and propose a novel chirp rate diversity-based linear frequency modulated (CRD-LFM) waveform design for approximating the desired MIMO radar transmit beampattern. To tackle the problem, we improve the classical two-step approach. Firstly we design the covariance matrix using the existing closed-form solution. Then we model the problem of obtaining the waveforms from the covariance matrix as a nonlinear optimization problem, and we utilize the Sequential Quadratic Programming (SQP) algorithm to solve it. The simulations demonstrate that the waveforms obtained by this method can well approximate the desired transmit beampattern and have higher range resolution. Furthermore, we verify that the suppression of auto-correlation sidelobe can be achieved using amplitude weighting, which is easily realizable in practical engineering.

Shrub Growth Improves Morphological Features of Nebkhas: A Case Study of Nitraria tangutorum in the Tengger Desert.

To understand the role of shrubs in nebkha development, a comparative analysis of nebkha morphology and shrub features was conducted in two different habitats at the southeast margin of the Tengger Desert, Northern China. Morphometric variables of 184 Nitraria tangutorum nebkhas were measured in a semi-fixed lake-basin lowland site (site 1, n = 102) and a salinized fixed sand site (site 2, n = 82). Mean length, width, projected area, and accumulated sand volume were all greater in nebkhas in site 1 than in site 2 (p < 0.05); however, mean height (i.e., sand burial depth) did not differ significantly in nebkhas between the two sites (p > 0.05). The larger nebkha volume in site 1 relative to site 2 (mean, 88.19 m3 vs. 33.16 m3) implied that the projected area influenced the accumulated sand volume. Nebkhas in site 1 tended to have large areas, low densities, and high spatial autocorrelation, while nebkhas in site 2 exhibited opposite trends with stochastic distribution. Mean vegetation density was significantly higher in site 1 than in site 2 (p < 0.05), while mean vegetation height exhibited an opposite trend (p < 0.05). In addition, there was higher vegetation coverage in site 1 than in site 2 (p > 0.05). According to the results, plant species (i.e., N. tangutorum) limited nebkha height under similar wind regimes regardless of the transport distance of aeolian material, while aeolian deposition and its effect on shrub growth jointly increased nebkha size.

Phenotypic and genotypic characterization of acaricide resistance in Rhipicephalus microplus field isolates from South Africa and Brazil

Rhipicephalus (Boophilus) microplus is one of the most successful ticks infesting cattle around the world. This highly-invasive species transmits cattle parasites that cause cattle fever leading to a high socio-economic burden. Tick eradication programs have often failed, due to the development of acaricide resistance. Here we characterize acaricide resistance in a large number of tick isolates from regions in South Africa (KwaZulu Natal, Mpumalanga, Western & Eastern Cape provinces) and two Brazilian regions.By means of Larval Packet Tests (LPT's) acaricide resistance was evaluated against five commonly used acaricides (chlorfenvinphos, fipronil, deltamethrin, amitraz, and ivermectin). Furthermore, the coding region containing the knock down resistance (kdr) mutation, known to result in pyrethroid resistance, was sequenced.Resistance to at least one acaricide class was reported in each of the five regions, and a high proportion of tick isolates exhibited multi-resistance to at least two acaricide classes (range: 22.2–80.0%). Furthermore, resistance ratios (RR) showed high spatial variation (intercontinental, as well as regional) but low regional spatial autocorrelation. Previous and current acaricide use correlated with current RR, and several combinations of acaricide RR were positively correlated. Moreover, fipronil resistance tended to be higher in farms with more intense acaricide use. The kdr-mutations provided the ticks a fitness advantage under the selection pressure of synthetic pyrethroids based on population (kdr-allele frequency) and individual level data (genotypes).The data show the threat of acaricide (multi-)resistance is high in Brazil and South Africa, but acaricide specific levels need to be assessed locally. For this purpose, gathering complementary molecular information on mutations that underlie resistance can reduce costs and expedite necessary actions. In an era of human-caused habitat alterations, implementing molecular data-driven programs becomes essential in overcoming tick-induced socio-economic losses.

Effects of compositional heterogeneity and spatial autocorrelation on richness and diversity in simulated landscapes.

Landscape structure plays a key role in mediating a variety of ecological processes affecting biodiversity patterns; however, its precise effects and the mechanisms underpinning them remain unclear. While the effects of landscape structure have been extensively investigated both empirically and theoretically from a metapopulation perspective, the effects of spatial structure at the landscape scale remain poorly explored from a metacommunity perspective. Here, we attempt to address this gap using a spatially explicit, individual-based metacommunity model to explore the effects of landscape compositional heterogeneity and per se spatial configuration on diversity at the landscape and patch levels via their influence on long-term community assembly processes. Our model simulates communities composed of species of annual, asexual organisms living, reproducing, dispersing, and competing within grid-based, fractal landscapes that vary in their magnitude of spatial environmental heterogeneity and in their degree of spatial environmental autocorrelation. Communities are additionally subject to temporal environmental fluctuations and external immigration, allowing for turnover in community composition. We found that compositional heterogeneity and spatial autocorrelation had differing effects on richness, diversity, and the landscape and patch scales. Landscape-level diversity was driven by community dissimilarity at the patch level and increased with greater heterogeneity, while landscape richness was largely the result of the short-term accumulation of immigrants and decreased with greater compositional heterogeneity. Both richness and diversity decreased in variance with greater compositional heterogeneity, indicating a reduction in community turnover over time. Patch-level richness and diversity patterns appeared to be driven by overall landscape richness and local mass effects, resulting in maximum patch-level richness and diversity at moderate levels of compositional heterogeneity and high spatial autocorrelation.

Dynamics of usage of menstrual hygiene and unhygienic methods among young women in India: a spatial analysis

BackgroundMenstruation, especially the menstrual cycle, is a vital sign for female adolescent health and maintaining menstrual hygiene is of utmost importance for menstruating girls and women. However, menstrual hygiene and management are issues that have not received adequate attention. Therefore, the present study aimed to explore spatial patterns of menstrual hygiene practices in India and to identify their socioeconomic and demographic determinants among women aged 15–24 years.MethodsThe study utilized data from the fifth round of the National Family Health Survey (NFHS-5) conducted during 2019–21 in India. The analysis was limited to 241,180 women aged 15–24 years. The statistical methods range from multinomial logistic regression, spatial autocorrelation in terms of Moran’s I statistics, to spatial regression in order to understand the spatial dependence and clustering in different methods of menstrual practices across the districts of India.ResultsAlmost half of the respondents (49.8 percent) reported using hygienic methods of bloodstain protection, while 22.7 percent still relied on unhygienic methods and 27.5 percent reported using both hygienic and unhygienic methods during their menstruation. Factors like age, place of residence, caste, religion, education, wealth index and toilet facility were found to be significantly associated with the use of unhygienic and both methods. It was also observed that the percentage of women practicing hygienic methods was predominantly higher in the Southern region. On the other hand, states like Madhya Pradesh and Bihar appeared to be hotspots for unhygienic menstrual practices. The univariate Moran’s I value for unhygienic and both methods were 0.722 and 0.596, respectively, depicting high spatial autocorrelation across districts in India. In spatial regression, rural residence, illiteracy, poverty, and no toilet facility were found to be statistically significant predictors of the use of unhygienic method and both methods.ConclusionYoung women should be educated about the importance of menstrual hygiene practices and the physiological consequences of unhygienic practices. Furthermore, interventions should target socio-economically disadvantaged women to increase the use of sanitary napkins.

Epidemiological characteristics and spatio-temporal analysis of brucellosis in Shandong province, 2015–2021

BackgroundBrucellosis is one of the major public health problems in China, it not only causes huge economic losses to the society, but also threatens the human’s physical and mental health. The reported cases of brucellosis in Shandong province were at a high level, therefore, it is necessary for us to understand the epidemic characteristics and distribution trend of Brucellosis in Shandong province. This study aims to describe the epidemiological characteristics and spatial clustering characteristics of brucellosis in Shandong Province, provide a reference for the scientific prevention and control.MethodsHuman brucellosis data in Shandong province from 2015 to 2021 were obtained from the China Information System for Disease Control and Prevention, the data were analyzed by descriptive epidemiological methods, spatial autocorrelation analysis and spatial-temporal cluster analysis methods use ArcGIS and SaTScan software, the results were presented in ArcMap.ResultsA total of 22,251 human cases of brucellosis were reported, the annual incidence ranged between 2.41/100,000 and 4.07/100,000 from 2015 to 2021 in Shandong province, incidence has been decreasing year by year, while there was a significant increase in 2021. The distribution of brucellosis was of a seasonal trend, mainly concentrating during March to August. The age of the cases was mainly concentrated in the 30–74 age ranges, the average annual incidence rate was significantly higher in males than in females. The spatial analysis showed that the epidemics were mainly concentrated in the north and southwest. For the spatial autocorrelation analysis, a high global autocorrelation was observed at the county level, and the high–high clusters mainly distributed in the north and southwest region. For the spatio-temporal scanning, the most likely cluster areas mainly distributed in the north area, and then gradually moved southward, and the radius of clustered narrowed.ConclusionsHuman brucellosis remains a common challenge, particularly in northern region in spring and summer. More disease prevention and control measures should be taken in high-risk populations, and such higher-risk susceptible areas to reduce the incidence of brucellosis and ensure the health of the people.

Surrogate-accelerated Bayesian framework for high-temperature thermal diffusivity characterization

Precise determination of thermal diffusivity at high temperatures is crucial for aerospace and energy industries. Periodic heating techniques such as Å ngström’s method are data-rich and commonly used for measuring thermal diffusivity of a solid material. In previous Å ngström’s method studies, regression techniques have been used to solve the associated inverse problem and to estimate thermal diffusivity by minimizing the residual between measurements and model predictions. This approach lacks rigorous uncertainty quantification and does not allow incorporation of prior knowledge for parameters. Adopting a Bayesian framework addresses these issues; however, probing the Bayesian posterior distribution is prohibitively expensive using Markov chain Monte Carlo (MCMC) methods, especially when the physical model is computationally expensive, as in the present study for which an analytical solution does not exist. This study employs a parametric surrogate model in the form of polynomial chaos to accelerate the physical model by several orders of magnitude to support Bayesian analysis. Moreover, high-temperature testing environments are difficult to control precisely, and many unknown parameters exist beyond the quantity of interest. Previous studies have employed random walks to set new parameters in the MCMC sampling process. However, random walks are inefficient in exploring high-dimensional parameter spaces and thus suffer high auto-correlation and poor convergence. To improve the efficiency of the MCMC sampler, this study employs a No-U-Turn sampler that explores the parameter space thoroughly and efficiently. We demonstrate the effectiveness of this Bayesian framework by analyzing experimental results on a graphite sample at approximately 1000 K.