Temporal Variance Research Articles

Exploring changes in the distribution of health status in type 2 diabetes over time - methodological aspects for estimating years lost due to disability for Germany

Abstract Knowledge of the severity distribution (SD) of certain diseases is crucial for calculating the years lost due to disability (YLD), as the time spent with a disease is explicitly weighted on the health state level. Depending on the disease, different severity levels are considered. In most cases, the SDs vary from mild, moderate, severe to most severe. This is different in the context of diabetes, where an explicit distinction is made according to the sequelae resulting from the disease progression. In general, these include diabetic neuropathy (with foot ulcers or amputations) and (mild or moderate) vision impairments including blindness due to diabetic retinopathy. These complications cause a considerable loss of quality of life for those affected and high costs for the healthcare system. However, the progression of many of the sequelae can be avoided through (secondary) preventive measures. Estimating the degree of severity is therefore an important indicator of the population affected and in need for care. Due to a lack of data, the Global Burden of Disease Study often uses time-invariant and regionally fixed SDs. Important insights as to the temporal variance of SDs thus remain unnoticed. As part of the BURDEN 2020 pilot project in Germany, SD for diabetes-related complications were estimated from routine health insurance data for the year 2017. In a follow-up project (BURDEN 2.0), these results will also be available for the years 2017 to 2022. If there are any changes in the proportion of individual health conditions, implications can be generated from routine healthcare data of those affected. The SDs overall and according to certain sequelae will be presented over time and discussed. Also, the resulting YLD over time for type 2 diabetes will be examined, including changes in the prevalence of the disease. This makes it possible to analyse and interpret the burden of disease due to diabetes during the years 2017 to 2022.

A Large Model-Derived Algorithm for Complex Organoids with Internal Morphogenesis and Digital Marker Derivation.

Automated segmentation and evaluation algorithms have been demonstrated to enhance the simplicity and translational utility of organoid technology. However, there is a pressing need for the development of complex organoids that possess epithelium environmental elements, dense regional cell aggregation, and intraorganoid morphologies. Nevertheless, there has been limited progress, including both the construction of data sets and the development of algorithms, in the use of user-friendly microscopy to address such complex organoids. In this study, a data set of bright-field and living cell fluorescence images in paired forms and with temporal variance was constructed using droplet-engineered lung organoids. Additionally, a large model-based algorithm was developed. Both the organoid contours and intraorganoid morphologies were included in the data set, and their physical parameters were included and screened to form multiplex digital markers for organoid evaluation. The algorithm has been demonstrated to outperform existing methods and is therefore suitable for the evaluation of complex organoids. It is expected that the algorithm will facilitate the successful demonstration of AI in organoid evaluation and decision-making regarding their status.

Spatio-temporal variability of soil water content across plot scales within a vineyard

Understanding spatio-temporal variability of soil water content (SWC) at different scales is of great importance for designing monitoring networks and assimilating observed data in hydrological modeling. The objective of the study was to examine spatio-temporal variability of SWC for different soil layers at different extent scales within an agricultural field. Spatio-temporal variability of SWC and the relationship between variability and mean were investigated at three extent scales for different soil layers of 0–100 cm in different years within an intensively irrigated vineyard. Nested sampling grids were used, and at each scale, there was a total of 16 points distributed regularly at the grids. The distance between any two adjacent sampling points was 25-m, 50-m, and 75-m for the three scales respectively. Overall, there was no consistent pattern regarding the change of mean, standard deviation (SD), and coefficient of variation (CV) with scales during the three years. For the surface soil layer, mean SWC was similar between scales, while SD was closely related to soil clay content at the sampling locations of each scale. For subsurface soils, the overall magnitude of variability of SWC was greater, whereas the difference in the variability between scales was lower than that of the soil texture of the corresponding soil layer. The proportional decrease of CV with increasing mean was found to be largely consistent between different years for all soil layers excluding 20–40 cm, but variable between scales and soil layers. For subsurface soil layers, the CV vs. mean relationship differed between relatively wet and dry years, indicating that factors controlling SWC dynamics for the layers below the surface layer were different depending on climatic conditions. Variance partitioning analysis showed that spatial variance accounted for at least 57 % of total variance for all cases, and the temporal variance at different scales fluctuated within a narrow range for subsurface soils for the three years. The results could benefit soil water content monitoring network design, calibration of soil moisture related parameters and assimilation of observed soil moisture data into field soil water modeling.

Variations in solar irradiance caused by solar cycle affect the temporal stability of ETM+ (LS7) ToA reflectance values at pseudo-invariant calibration sites (PICS)

ABSTRACT The aim of this study is to quantify the impact of the effect of long-term solar cycle variations in solar irradiance on the accuracy and temporal stability of ToA reflectance values from Enhanced Thematic Mapper Plus (ETM+/L7) sensor images. We used 1382 ETM+ scenes corresponding to five Pseudo-Invariant Calibration Sites (PICS), obtained between 2003 and 2020. Solar irradiance data from the Spectral Irradiance Monitor (SIM/SORCE) were used to calculate daily values of mean exoatmospheric solar irradiance (ESUN), implemented in the radiometric correction of ToA reflectance. Enhanced temporal stability coefficients and variance tests were calculated to evaluate the stability of the time series obtained. The results indicate that the use of daily SIM/SORCE solar spectra has a significant impact (alpha <0.001) on radiometric correction and the accuracy of ToA reflectance values. In radiometric calibration, the percentage of top of atmosphere temporal stability that can be recovered based on 5 Sahara PICS ETM+/L7 scenes can exceed 0.75%, 0.50%, 0.39%, 0.45%, 0.30% and 0.25% in the blue, green, red, NIR, SWIR-1 and SWIR-2 spectral bands, respectively, and is not considered statistically significant (alpha >0.05). We concluded that considering the effects of solar cycles during the radiometric calibration of multispectral sensors contributes to improving the long-term stability of ToA reflectance products.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by ‘electrophysiology-invisible’ signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by 'electrophysiology-invisible' signals. These findings offer a novel perspective on our understanding of RSN interpretation.

A Study of Seasonal and Temporal Variances in Ambient Air Quality of Highly Polluted Cities in Rajasthan

The quality of the urban environment in tropical and subtropical densely populated cities is a complicated subject that has garnered a lot of attention in the current setting. Some of the most polluted cities in Rajasthan are Bhiwadi, Jaipur, Kota, and Udaipur, where the air quality has drastically declined over the previous ten years, according to an IQAir report. In order to determine the seasonal and temporal fluctuations in the concentrations of major air pollutants, such as carbon monoxide (CO), ozone (O3), nitrogen dioxide (NO2), sulphur dioxide (SO2), and particulate matter (PM10 and PM2.5), in an urban setting in Rajasthan, this study will examine the ambient air quality in severely polluted cities throughout the state. A comprehensive investigation of the seasonal and temporal variations in ambient air quality throughout Rajasthan's extremely polluted cities was made possible by the application of PCA and the K-Means Clustering Algorithm. We interpreted the intricate patterns of pollution oscillations by means of rigorous time-series analysis, providing insight into the dynamic interactions among meteorological conditions, sources of pollution, and regulatory actions. The results indicate that there were more seasonal variations during the summer, and that levels of particulate matter (PM10 and PM2.5) and nitrogen dioxide (NO2) in places like Jaipur, Bhiwadi, Kota, and Udaipur alarmingly rose above pre-pandemic levels. This highlights the significance of identifying and addressing the various challenges caused by air pollution at different times of the year and in different seasons. Furthermore, identifying the main sources of pollution and assessing the effectiveness of current legislation offer insightful information for focused actions.

The genome-wide signature of short-term temporal selection

Despite evolutionary biology's obsession with natural selection, few studies have evaluated multigenerational series of patterns of selection on a genome-wide scale in natural populations. Here, we report on a 10-y population-genomic survey of the microcrustacean Daphnia pulex. The genome sequences of [Formula: see text]800 isolates provide insights into patterns of selection that cannot be obtained from long-term molecular-evolution studies, including the following: the pervasiveness of near quasi-neutrality across the genome (mean net selection coefficients near zero, but with significant temporal variance about the mean, and little evidence of positive covariance of selection across time intervals); the preponderance of weak positive selection operating on minor alleles; and a genome-wide distribution of numerous small linkage islands of observable selection influencing levels of nucleotide diversity. These results suggest that interannual fluctuating selection is a major determinant of standing levels of variation in natural populations, challenge the conventional paradigm for interpreting patterns of nucleotide diversity and divergence, and motivate the need for the further development of theoretical expressions for the interpretation of population-genomic data.

Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity

Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH’s flawed window-based spatial analysis and underutilized temporal component. In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversity-plant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.

Spatial–Temporal and Risk Assessment of Microplastics in the Surface Water of the Qinhuai River during Different Rainfall Seasons in Nanjing City, China

Microplastics (MPs) are increasingly becoming recognized as worldwide environmental contaminants, exerting a substantial impact on the safety of city rivers. This study explored the temporal variance in MPs in different rainfall seasons, including spring, plum, and autumn rains. The Qinhuai River has large spatial fluctuations in MPs at six sampling sites, with an average concentration of 466.62 ± 153.69 items/L, and higher MP abundance was found downstream of spring rain and upstream of autumn rain. Among the different rainfall seasons, the variations in microplastics at various sampling sites were more stable in the plum rain season, with an average concentration of 473.67 ± 105.17 items/L, while the concentrations of TP and TN in the plum rain season were higher than those in other rain seasons. Transparent MPs had the highest abundance at many sampling sites in all seasons, and large-sized MPs (270–5000 μm) occurred more in the autumn rain season. PVC was more prevalent in autumn, but PET decreased in the plum rain season. Interestingly, more fibers, PET, and large-sized MPs were found in the autumn rain. The index of hazard scores of plastic polymers (H) revealed that the studied river was at a severe pollution level (IV), which was highly influenced by PVC and PC. In addition, the pollution load index (PLI) value in different rain seasons indicated slight pollution (I). At the same time, it was higher in autumn rains than in other seasons due to the higher variance in MPs. Therefore, the ecological risk of microplastics in the Qinhuai River should be seriously considered, along with seasonal variance and the PVC and PC polymers. Our research is expected to provide valuable assistance in improving the management of urban rivers.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by "electrophysiology-invisible" signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Disparity in temporal and spatial relationships between resting-state electrophysiological and fMRI signals.

Resting-state brain networks (RSNs) have been widely applied in health and disease, but the interpretation of RSNs in terms of the underlying neural activity is unclear. To address this fundamental question, we conducted simultaneous recordings of whole-brain resting-state functional magnetic resonance imaging (rsfMRI) and electrophysiology signals in two separate brain regions of rats. Our data reveal that for both recording sites, spatial maps derived from band-specific local field potential (LFP) power can account for up to 90% of the spatial variability in RSNs derived from rsfMRI signals. Surprisingly, the time series of LFP band power can only explain to a maximum of 35% of the temporal variance of the local rsfMRI time course from the same site. In addition, regressing out time series of LFP power from rsfMRI signals has minimal impact on the spatial patterns of rsfMRI-based RSNs. This disparity in the spatial and temporal relationships between resting-state electrophysiology and rsfMRI signals suggests that electrophysiological activity alone does not fully explain the effects observed in the rsfMRI signal, implying the existence of an rsfMRI component contributed by "electrophysiology-invisible" signals. These findings offer a novel perspective on our understanding of RSN interpretation.

Research on the Spatial-Temporal Evolution and Driving Mechanism of Coupling Coordination among Digital-Tourism-Environment in the Yellow River Basin

The digital economy, the tourism industry, and ecological protection are closely related, which holds paramount significance for regional sustainable development. Based on panel data from 2011 to 2021 in the Yellow River Basin, the study scrutinizes the spatial and temporal variances and driving factors of the three-system coupling coordination degree. The results indicate that: (1) The development level of the three systems is significantly different. The coupling coordination degree shifts from imminent imbalance to marginal coordination, but there is a noticeable expansion in spatial disparities. (2) The coupling coordination degree of non-resource cities significantly surpasses that of resource cities, and regenerative cities outperform growing, mature, and declining cities. The overall pattern of the provincial capital cities “center-periphery”, decreased the coordination characteristics. The spatial correlation of coupling coordination degree presents two aggregation patterns of “high-high” and “low-low”, and the overall correlation gradually weakens. (3) The driving factors reveal that the economic driving force is the largest, followed by the internal driving force, and the government regulatory force and scientific and technological driving force are relatively weak. Consequently, this paper proposes to strengthen the integration and penetration function of the digital economy and have it play the linkage role between regions. This lays down a solid foundation for crafting scientifically informed policies aimed at achieving regional sustainable development.

Dominant heat transfer mechanism with conical roughness in a cubical box in turbulent Rayleigh–Bénard convection

In the present study, we numerically investigate the effect of Prandtl number on the heat transfer mechanism in turbulent Rayleigh–Bénard convection inside a cubical box endowed with conical roughness elements. The Rayleigh number is kept fixed at Ra=108, while the Prandtl number (Pr) varies from 1 to 50. In contrast to the monotonic increasing trend of the Nusselt number (Nu∼Pr0.27) in the two-dimensional (2D) roughness explored previously [Sharma et al., “Influence of Prandtl number in turbulent Rayleigh-Bénard convection over rough surfaces,” Phys. Rev. Fluids 7, 104609 (2022)], it assumes an invariant behavior (∼Pr0.012) for three dimensional (3D), though it is approximately 50% higher than its smooth counterpart. Flow intensity, measured in terms of Reynolds number (Re), drops with increasing Pr showing consistently lower magnitude for the 3D configuration. The addition of roughness elements is observed to disrupt the preferred orientation of large-scale circulation (LSC). The effect is predominant for lower Pr, where the roughness interferes most with the natural bias of LSC toward the diagonal planes of the cubical box. The analysis of plume statistics reveals that both coverage and intensity of plumes are augmented for the roughened cell. Increased homogeneity in the flow at higher Pr is reflected by the emergence of a more pronounced and distinguishable peak in probability density functions of temperature and velocity. Temporal spectra and variance data substantiate augmented intensity of fluctuations in the rough cell, while behavioral differences in the flow at different Pr are elucidated by using cross correlation of vertical velocity and temperature fluctuations.

Temporal dynamics of drinking water sodium levels in coastal areas, Cyprus 2009–2020

Around the world, groundwater salinity levels are increasing in coastal areas, as a result of its systematic overexploitation for domestic, agricultural and industrial demand and potentially due to climate change manifestations (such as, sea level rise). We hypothesized that the groundwater quality of many Mediterranean coastal areas is already being perturbed, especially for water salinity, depending on the groundwater distance from the seafront. The objectives of this study were: i) to evaluate the magnitude and temporal variance of drinking water sodium (Na) as a metric of salt intake used for public health purposes using drinking water data in Cyprus; and ii) to examine the degree of Na enrichment in drinking water as defined by the seawater coastline distance of each sampling point. Open access governmental data of drinking water Na (n = 3304), daily max ambient air temperature and total rainfall were obtained for the period of 2009–2020 from governmental repositories. Linear mixed-effect regression models of drinking water Na with unsupervised covariance matrix were used. After adjusting for temperature and rainfall data, there was a significant annual increase in drinking water Na levels over time (beta = 0.01; 95 % CI: 0.00, 0.02; p = 0.02) for the coastal areas (<10 km from coastline, cutoff used by the EU Environment Agency), but this was not the case for non-coastal areas (>10 km distance from coastline). The distance of each sampling point from the coastline in Cyprus was negatively associated with drinking water Na in coastal areas (beta = −0.04, 95%CI: −0.06, −0.01; p = 0.002); this was not the case for non-coastal areas. More research is warranted to better understand the impacts of global environmental change on water quality in association with the burden of disease in coastal areas.

Spatial and temporal variation and convergence in the efficiency of high-standard farmland construction: Evidence in China

To assess the efficiency of high-standard farmland construction (HSFC), this study utilized data spanning from 1998 to 2020 obtained from 30 provinces, autonomous regions, and municipalities in China. This assessment was conducted using a three-stage super-efficient slack-based measure data envelopment analysis(SBM-DEA)model. Furthermore, we investigated the spatial and temporal variances and convergence of HSFC efficiency using the Dagum Gini coefficient and convergence analysis. The results firstly show that from 2007 to 2020, China's HSFC efficiency displayed fluctuations and declines, significantly influenced by environmental and stochastic factors. Secondly, it showed that the gap in China's HSFC efficiency has widened, with super-variable density being demonstrated as the primary source of spatial imbalance in HSFC efficiency. Thirdly, it showed that the deviation in the efficiency of HSFC in the central region of China from the average level has shown a gradually declining trend, while the deviation in the efficiency of HSFC in the national, western, eastern, and northeastern regions has not presented a downward trend. Fourthly, it demonstrated that over time, the efficiency of building high-standard agriculture throughout the nation and its four main areas will converge to the same level.

Assimilation of satellite swaths versus daily means of sea ice concentration in a regional coupled ocean–sea ice model

Abstract. Operational forecasting systems routinely assimilate daily means of sea ice concentration (SIC) from microwave radiometers in order to improve the accuracy of the forecasts. However, the temporal and spatial averaging of the individual satellite swaths into daily means of SIC entails two main drawbacks: (i) the spatial resolution of the original product is blurred (especially critical in periods with strong sub-daily sea ice movement), and (ii) the sub-daily frequency of passive microwave observations in the Arctic are not used, providing less temporal resolution in the data assimilation (DA) analysis and, therefore, in the forecast. Within the SIRANO (Sea Ice Retrievals and data Assimilation in NOrway) project, we investigate how challenges (i) and (ii) can be avoided by assimilating individual satellite swaths (level 3 uncollated) instead of daily means (level 3) of SIC. To do so, we use a regional configuration of the Barents Sea (2.5 km grid) based on the Regional Ocean Modeling System (ROMS) and the Los Alamos Sea Ice Model (CICE) together with the ensemble Kalman filter (EnKF) as the DA system. The assimilation of individual swaths significantly improves the EnKF analysis of SIC compared to the assimilation of daily means; the mean absolute difference (MAD) shows a 10 % improvement at the end of the assimilation period and a 7 % improvement at the end of the 7 d forecast period. This improvement is caused by better exploitation of the information provided by the SIC swath data, in terms of both spatial and temporal variance, compared to the case when the swaths are combined to form a daily mean before assimilation.

Using remote sensing to map degraded mountain peatlands with high climate mitigation potential in Colombia's Central Cordillera

Peatlands are the most carbon-dense ecosystems on earth. In tropical mountains, peatlands are numerous and susceptible to rapid degradation and carbon loss after human disturbances. Quantifying where peatlands are located and how they are affected by land use is key in creating a baseline of carbon stocks and greenhouse gas fluxes from tropical mountain peatlands. However, mapping peatlands in the páramo of the Northern Andes is difficult because they are in a topographically complex environment with nearly continuous cloud cover and frequent conversion to pastures or cropland. The goal of this effort was to identify the different types of páramo peatlands and their degradation patterns in the Colombian Central Cordillera. Moderate resolution cloud-free composites of optical imagery, temporal variance in ALOS- PALSAR L-band SAR, Sentinel-1 C-band SAR, and topography data were used as inputs in a machine learning classifier to identify was used to map 12 land cover classes including peatlands with natural vegetation and peatlands converted to pasture. Field data from 507 control points collected across the study area, including information on the vegetation and carbon content on the top 20 cm of the soil, were used to train and validate the classifier. Results show that the use of multiple platforms and image dates, including variance of the radar returns, is necessary for a clear separation of disturbed and undisturbed peatland classes. Peatland area varied across the study region, covering 7% and 20% of the landscape in the northern and southern portions of the study area, respectively. Disturbed peatlands with exotic grasses cover nearly 2% of the area. The overall accuracy of the peatland classes was 82.6%. Disturbed peatlands with exotic grasses had less carbon in the top 20 cm than undisturbed peatlands with natural vegetation. These results highlight the prevalence of peatlands in the tropical Andes and a promising approach to detecting peatlands converted to agriculture. Understanding the distribution and extent of these carbon dense ecosystems can facilitate the restoration and protection of peatlands in the northern Andes, with implications for the future trajectories of the national greenhouse gas inventory.

Spatial and temporal variance in public perception of electric vehicles: A comparative analysis of adoption pioneers and laggards using twitter data

Promotional strategies for electric vehicle (EV) adoption need to be tailored to the public's perception of these vehicles. However, there is a notable gap in understanding how public perception of EVs differs between adoption pioneers and those slower to adopt (“adoption laggards”) in terms of the percentage of registered EVs. This understanding is vital for the development of effective promotional strategies, especially for adoption laggards. To address this research gap, this study compares various public perception of EVs among adoption pioneers and laggards in U.S. states by analyzing historical Twitter discussions using Natural Language Processing (NLP) techniques, including topic and sentiment analysis. The findings reveal distinct patterns in online EV discussions between the two groups, with adoption laggards being concerned more about affordability and gas prices, and adoption pioneers consistently discussing various topics covering EV and battery manufacturing, charging infrastructure, government EV initiatives, and the EV market. Adoption pioneers exhibit stable, positive attitudes, while adoption laggards display frequent attitude fluctuations, including more negative ones recently. These differences emphasize the importance of EV affordability for adoption laggards and indicate a possible widening gap in EV adoption due to ongoing debates among adoption laggards surrounding the value of transportation electrification. By addressing adoption laggards' concerns and promoting the benefits of EVs, policymakers and stakeholders can enhance perceptions toward EVs, bridging the gap between adoption pioneers and laggards, and promoting widespread adoption of cleaner transportation options.

Spatiotemporal Variation and Prediction Analysis of Land Use/Land Cover and Ecosystem Service Changes in Gannan, China

For the preservation of ecosystems, including the enhancement of ecological strategies, examining the temporal and geographical variance in ecosystem services (ESs) and land use/land cover change (LUCC) is crucial. Gannan is situated on the upper Yellow River, which is a notable water conservation region with excellent ecological quality, but in the background of the local traditional production mode and rapid economic development, natural disasters, grassland degradation, and other ecological problems occur frequently. The integrated valuing of ecosystem services and tradeoffs (InVEST) model and the patch-generating land use simulation (PLUS) model are combined in this work to assess the spatiotemporal variance in ESs in Gannan. We set up three scenarios in modeling future land use—ecological protection (EP), natural development (ND), and economic development (ED) in 2050—and analyzed and evaluated the drivers of the variation in ESs. In order to reveal the LUCC in Gannan between 1990 and 2020, we predicted the LUCC and ESs spatial distribution characteristics in 2050, explored the correlation between its driving factors, and comprehensively analyzed and propose optimization measures and protection strategies. Through several simulation experiments, the findings indicate the following: (1) the largest percentage of land expansion for construction in Gannan between 1990 and 2020 is 74.53%, and the most noticeable percentage of shrinkage in the sand area is 20.67%; (2) from 1990 to 2020, Gannan’s water yield, carbon storage, soil retention, and habitat quality all changed, by 60 × 108 m3, 0.04 × 108 t, −10.66 × 108 t, and −0.02, respectively; (3) ESs are influenced by a variety of natural and societal variables: the southern and southwestern regions of Gannan are home to the majority of ESs hot spot areas, while the northern region is home to the majority of cold spot areas. This study contributes to the analysis of the developmental traits of Gannan ecosystems and can serve as a model for the preservation of terrestrial ecosystems with comparable environmental traits.