Changes In Temperature Patterns Research Articles

Temporal and Spatial Patterns of Changes in Thermal Regime of the Rivers in the Northeast of the Asian Part of Russia. 1. Assessment of Changes in the Water Temperature

Temporal and Spatial Patterns of Changes in Thermal Regime of the Rivers in the Northeast of the Asian Part of Russia. 1. Assessment of Changes in the Water Temperature

Imaging Phenotypes of Stress Based on Functional MRI

Background: Stress is an adaptive response to daily challenges faced by the body to maintain its internal homeostasis. The pathophysiology of stress has been described based on the role of the sympatho-adrenergic system and the hypothalamic-pituitary- adrenal axis. Few studies have been done describing the role of functional MRI and PET to study the patterns of brain involvement in symptomatic patients. Methods: The author studied 50 post-acute covid-19 syndrome patients one year after the acute episodes along with 10 healthy control patients. Based on the patterns of stress on imaging the patients were phenotyped on the severity of the changes of stress into a classification system. Results: The results of the study showed a distinct temporal pattern of changes based on the severity of chronic stress. Class II and III Group patients on imaging formed the largest number 32(64%) and also showed good correlation with PCS-10 scores which were moderate in this group. The supra optic nucleus followed by para ventricular nuclei were the commonest affected during stress. The study showed that involvement of DMN and SALIEN was seen as the severity of stress progressed to a burn out stage IV which resulted in hypoactivation of all major networks and hypothalamic nuclei. Hippocampal and amygdala volumetric changes alongwith altered asymmetry was seen in class II-IV groups. Conclusion: The author concluded that functional MRI could detect the pattern of changes of stress and the findings not only can be helpful in the diagnosis but also in planning management of these patients.

Modeling spatio temporal pattern of urban land use and land cover change by using geospatial technology: a case of Shambu Town, Horo Guduru Wallaga, Ethiopia

Modeling spatio temporal pattern of urban land use and land cover change by using geospatial technology: a case of Shambu Town, Horo Guduru Wallaga, Ethiopia

Characterizing 4 decades of accelerated glacial mass loss in the west Nyainqentanglha Range of the Tibetan Plateau

Abstract. Glacier retreat is altering the water regime of the Tibetan Plateau (TP) as the region's climate changes, but there remain substantial gaps in our knowledge of recent glacier loss in this region due to the difficulty of making direct high-mountain observations, and this limits our ability to predict the future of this important water resource. Here, we assessed 44 years of glacier area and volume changes in the major west Nyainqentanglha Range (WNR) that supplies meltwater to the densely populated Lhasa River basin and Nam Co, the second largest endorheic lake on the TP. Between the two periods 1976–2000 and 2000–2020, we found that the glacier areal retreat rate more than doubled (from -0.54±0.21 % a−1 to -1.17±0.30 % a−1), and surface lowering also accelerated (from -0.26±0.09 to -0.37±0.15 m w.e a−1) with particularly intense melting after 2014. This acceleration is similar in both timing and magnitude to that observed for Himalayan glaciers farther south. Besides, the areal retreat rate and mass loss rate of most glaciers in the WNR were not synchronized. To understand the sensitivity of WNR glaciers to climate forcing, we examined the effects of topography, debris cover and the presence of proglacial lakes on our observed changes. We found consistently faster areal retreat but slower thinning rates on steeper slopes and an inconsistent relationship with aspect. We concluded that our observed spatial and temporal patterns of glacier change were dominated by observed local variations in temperature and precipitation, the melt-reducing role of supraglacial debris, and the increasing influence of ice-marginal lakes on glacier ablation.

Time-Dependent Changes in the Biofluid Levels of Neural Injury Markers in Severe Traumatic Brain Injury Patients-Cerebrospinal Fluid and Cerebral Microdialysates: A Longitudinal Prospective Pilot Study.

Monitoring protein biomarker levels in the cerebrospinal fluid (CSF) can help assess injury severity and outcome after traumatic brain injury (TBI). Determining injury-induced changes in the proteome of brain extracellular fluid (bECF) can more closely reflect changes in the brain parenchyma, but bECF is not routinely available. The aim of this pilot study was to compare time-dependent changes of S100 calcium-binding protein B (S100B), neuron-specific enolase (NSE), total Tau, and phosphorylated Tau (p-Tau) levels in matching CSF and bECF samples collected at 1, 3, and 5 days post-injury from severe TBI patients (n = 7; GCS 3-8) using microcapillary-based western analysis. We found that time-dependent changes in CSF and bECF levels were most pronounced for S100B and NSE, but there was substantial patient-to-patient variability. Importantly, the temporal pattern of biomarker changes in CSF and bECF samples showed similar trends. We also detected two different immunoreactive forms of S100B in both CSF and bECF samples, but the contribution of the different immunoreactive forms to total immunoreactivity varied from patient to patient and time point to time point. Our study is limited, but it illustrates the value of both quantitative and qualitative analysis of protein biomarkers and the importance of serial sampling for biofluid analysis after severe TBI.

A new rainfall prediction model based on ICEEMDAN-WSD-BiLSTM and ESN.

Precipitation, as an important indicator describing the evolution of the regional climate system, plays an important role in understanding the spatial and temporal distribution characteristics of regional precipitation. Scientific and accurate prediction of regional precipitation is helpful to provide theoretical basis for relevant departments to guide flood and drought control. To address the uncertainty and nonlinear characteristics of precipitation series, this paper uses the established improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN)-wavelet signal denoising (WSD)-bi-directional long short-term memory (BiLSTM), and echo state network (ESN) models to predict precipitation of four cities in southern Anhui Province. The BiLSTM is used to predict the high-frequency components and the ESN to predict the low-frequency components, thus avoiding the influence between the two neural network predictions. The results show that the ICEEMDAN-WSD-BiLSTM and ESN models are more accurate. The average relative error reached 2.64% and the NSE (Nash-Sutcliffe efficiency coefficient) was 0.91, which was significantly better than the other four models. The model reveals the temporal change pattern and evolution characteristics of future precipitation, guides flood prevention and mitigation, and has certain theoretical significance and application value for promoting regional sustainable development.

Ecological Risk Assessment and Prediction Based on Scale Optimization—A Case Study of Nanning, a Landscape Garden City in China

Analysis and prediction of urban ecological risk are crucial means for resolving the dichotomy between ecological preservation and economic development, thereby enhancing regional ecological security and fostering sustainable development. This study uses Nanning, a Chinese landscape garden city, as an example. Based on spatial granularity and extent perspectives, using 30 m land use data, the optimal scale for an ecological risk assessment (ERA) and prediction is confirmed. This study also explores the patterns of spatial and temporal changes in ecological risk in Nanning on the optimal scale. At the same time, the Patch-generating Land Use Simulation model is used to predict Nanning’s ecological risk in 2036 under two scenarios and to propose ecological conservation recommendations in light of the study results. The study results show that: a spatial granularity of 120 m and a spatial extent of 7 km are the best scales for ERA and prediction in Nanning. Although the spatial distribution of ecological risk levels is obviously different, the overall ecological risk is relatively low, and under the scenario of ecological protection in 2036, the area of high ecological risk in Nanning is small. The results can provide theoretical support for ERA and the prediction of landscape cities and ecological civilization construction.

Discovering hematoma-stimulated circuits for secondary brain injury after intraventricular hemorrhage by spatial transcriptome analysis.

Central nervous system (CNS) diseases, such as neurodegenerative disorders and brain diseases caused by acute injuries, are important, yet challenging to study due to disease lesion locations and other complexities. Utilizing the powerful method of spatial transcriptome analysis together with novel algorithms we developed for the study, we report here for the first time a 3D trajectory map of gene expression changes in the brain following acute neural injury using a mouse model of intraventricular hemorrhage (IVH). IVH is a common and representative complication after various acute brain injuries with severe mortality and mobility implications. Our data identified three main 3D global pseudospace-time trajectory bundles that represent the main neural circuits from the lateral ventricle to the hippocampus and primary cortex affected by experimental IVH stimulation. Further analysis indicated a rapid response in the primary cortex, as well as a direct and integrated effect on the hippocampus after IVH stimulation. These results are informative for understanding the pathophysiological changes, including the spatial and temporal patterns of gene expression changes, in IVH patients after acute brain injury, strategizing more effective clinical management regimens, and developing novel bioinformatics strategies for the study of other CNS diseases. The algorithm strategies used in this study are searchable via a web service (www.combio-lezhang.online/3dstivh/home).

Experimental Study on Electrical Properties of Power Lithium-ion Battery

The paper studies the surface temperature rise and pulse charge/discharge capacity of batteries with different charge/discharge rates under different temperature conditions. The discharge performance and charge rate characteristics of an automotive lithium-ion battery were investigated. The battery discharge performance tests were conducted under different ambient temperatures and different charge rate conditions to obtain the battery charge voltage curve, charge capacity, and the temperature change pattern of the external surface of the battery during the charging process. Also, internal resistances of the battery cell were measured using the hybrid pulse power characteristic (HPPC). Experimental results show that the battery’s 10% to 90% DOD range shows the excellent pulse charging and discharging ability of the battery, and the temperature change of the battery is much higher at the beginning of the charging process and the end of the discharging process due to the influence of internal resistance.

Estimating Temporally Variable Selection Intensity from Ancient DNA Data.

Novel technologies for recovering DNA information from archaeological and historical specimens have made available an ever-increasing amount of temporally spaced genetic samples from natural populations. These genetic time series permit the direct assessment of patterns of temporal changes in allele frequencies and hold the promise of improving power for the inference of selection. Increased time resolution can further facilitate testing hypotheses regarding the drivers of past selection events such as the incidence of plant and animal domestication. However, studying past selection processes through ancient DNA (aDNA) still involves considerable obstacles such as postmortem damage, high fragmentation, low coverage, and small samples. To circumvent these challenges, we introduce a novel Bayesian framework for the inference of temporally variable selection based on genotype likelihoods instead of allele frequencies, thereby enabling us to model sample uncertainties resulting from the damage and fragmentation of aDNA molecules. Also, our approach permits the reconstruction of the underlying allele frequency trajectories of the population through time, which allows for a better understanding of the drivers of selection. We evaluate its performance through extensive simulations and demonstrate its utility with an application to the ancient horse samples genotyped at the loci for coat coloration. Our results reveal that incorporating sample uncertainties can further improve the inference of selection.

Analysis of the Evolution of Land-Use Types in the Qilian Mountains from 1980 to 2020

The Qilian Mountains (QMs), located in the northeast part of the Qinghai–Tibetan Plateau in China, have a fragile ecological environment, complex and sensitive climate, and diverse land-cover types. It plays an important role in the “Qinghai–Tibetan Plateau Ecological Barrier” and “Northern Sand Control Belt” in China’s “two screens and three belts” ecological security strategy. Based on land use data of 1980, 1990, 1995, 2000, 2005, 2010, 2015, and 2020, we utilized GIS technology, land use dynamic degree, and land use transition matrixes to analyze the spatial and temporal evolution of land use in the QMs from 1980 to 2020. The results showed the following: (1) From 1980 to 2020, grassland, forest land, and unused land were the main land-use types in the QMs, and the proportion of construction land accounted for only 0.31% of all land-use types. (2) The single land use dynamic degree showed that the dynamic degree of construction land was the highest and the fastest change rate from 2010 to 2015. The comprehensive land use dynamic degree showed that the intensity of land-use change was relatively drastic in the three time periods of 1990–1995, 1995–2000, and 2015–2020. (3) The land-use types in the study area switched infrequently during 2000–2005, 2005–2010, and 2010–2015. (4) The main transition directions of land-use types were grassland and unused land to other land-use types. These changes altered the spatial distributions of different land-use types. The study is critical for understanding the spatial and temporal change patterns of land-use change in the QMs and providing guidance for the optimization of land use in the study area and the improvement of regional eco-environmental protection.

Plastic plumage colouration in response to experimental humidity supports Gloger’s rule

Knowing how animals adapt their phenotype to local temperature and humidity is key to understanding not only ecogeographical rules, but also how species will manage climate change, as current models predict changes in global patterns of temperature and precipitation. In endotherms, colour adaptations in response to climate have been under investigated, and their acclimatization-the individual capacity to reversibly adjust phenotype in response to different environments-is unknown. Geographic trends can provide clues about abiotic variables involved in colouration, as postulated by Gloger’s rule, which predicts darker individuals in warm and humid regions. We tested whether house sparrows (Passer domesticus) can adjust colouration when faced with varying humidity conditions. We exposed birds to either a dry (humidity 45%) or a wet environment (70%) six months before their moult, and measured colouration in newly developed feathers in five parts of the body (bib, crown, crown stripe, belly and rump). As predicted by Gloger’s rule, birds in wet conditions developed darker (bib and belly) and larger (bib) melanised plumage patches, than birds in dry conditions. Our result provides the first unequivocal evidence that the ability of individual birds to adjust their colouration may be a potential adaptation to climatic changes in endotherms.

Process hazard assessment of energetic ionic liquid with kinetic evaluation and thermal equilibrium

With the continuous development of battery technology, there are new research investments in materials of various parts. In the field of electrolytes, ionic liquids (IL) are considered to be excellent electrolytes and have been widely studied in distinct energy fields. However, it is necessary to pay attention to the safety characteristics of ionic liquids at high temperature due to the application of energy, but there is little research on the reaction and kinetics of ionic liquids. To ensure the safety of ionic liquids, such as high temperature, the common ionic liquid 1-Ethyl-3-methylimidazolium nitrate ([Emim] NO3) was selected for analysis. The exothermic mode is obtained from the data of differential scanning calorimetry. The basic reaction parameters of [Emim] NO3 were determined with thermodynamic equation simulation. For ionic liquids in the actual situation, consider adding a heat balance model to estimate its temperature change pattern and find out the hazard temperature and related safety parameters. Temperature changes were estimated by constructing 25.0 g and 50.0 g packages to simulate material reactions and heat transfer in the external environment. The results showed that [Emim] NO3 had shorter TMRad and TCL (<1 day) when the temperature was above 180 °C.

Long Short-Term Fusion Spatial-Temporal Graph Convolutional Networks for Traffic Flow Forecasting

Traffic flow forecasting, as one of the important components of intelligent transport systems (ITS), plays an indispensable role in a wide range of applications such as traffic management and city planning. However, complex spatial dependencies and dynamic changes in temporal patterns exist between different routes, and obtaining as many spatial-temporal features and dependencies as possible from node data has been a challenging task in traffic flow prediction. Current approaches typically use independent modules to treat temporal and spatial correlations separately without synchronously capturing such spatial-temporal correlations, or focus only on local spatial-temporal dependencies, thereby ignoring the implied long-term spatial-temporal periodicity. With this in mind, this paper proposes a long-term spatial-temporal graph convolutional fusion network (LSTFGCN) for traffic flow prediction modeling. First, we designed a synchronous spatial-temporal feature capture module, which can fruitfully extract the complex local spatial-temporal dependence of nodes. Second, we designed an ordinary differential equation graph convolution (ODEGCN) to capture more long-term spatial-temporal dependence using the spatial-temporal graph convolution of ordinary differential equation. At the same time, by integrating in parallel the ODEGCN, the spatial-temporal graph convolution attention module (GCAM), and the gated convolution module, we can effectively make the model learn more long short-term spatial-temporal dependencies in the processing of spatial-temporal sequences.Our experimental results on multiple public traffic datasets show that our method consistently obtained the optimal performance compared to the other baselines.

Basin-scale terrestrial water storage changes inferred from GRACE-based geopotential differences: a case study of the Yangtze River Basin, China

SUMMARY The Gravity Recovery and Climate Experiment (GRACE) mass concentration (mascon) solutions provide enhanced signal and spatial resolution of surface mass changes by using regularization techniques to reduce striping errors. To further improve the computational efficiency and capture the same benefits as GRACE mascon solutions, we presented an estimation of regional mascon solutions from GRACE-based geopotential differences by using spatio-temporal constraints with the unconstrained spherical harmonic solutions as a priori information. As a case study, the changes in the basin-scale terrestrial water storage (TWS) over the Yangtze River Basin (YRB) on 2° × 2° grids at monthly intervals were estimated using GRACE-based geopotential differences, for the period of 2003 January—2013 December. The estimates were validated through official GRACE mascon solutions and in situ observations (i.e. time derivative of TWS change derived from precipitation, evapotranspiration and river run-off based on the water mass balance equation). The results demonstrate that the spatial and temporal patterns of TWS changes in the YRB inferred from geopotential differences adequately agree with the official mascon solutions; however, differences in amplitudes can be observed at the subbasin scale because of different regularizations applied in different solutions. In situ validations demonstrate that seasonal changes of mascon solutions and in situ observations agree well in the YRB; however, there are evident discrepancies in amplitudes over the subbasins owing to leakage biases in mascon solutions. For the entire YRB, the statistical evaluation and cross-wavelet transform demonstrate that our regional mascon solutions appear more consistent with in situ observations than the official mascon solutions. In addition, compared with the results estimated by spatial constraints, regional mascon solutions estimated by spatio-temporal constraints using observations from three consecutive months adjacent to the given month were improved. Our method provides an alternative option to use different regularization constraints, which is helpful for fine-tuning analysis of basin-scale TWS changes.

The dynamics of normalized difference vegetation index (NDVI) of natural and agricultural landscapes in Alexandrovsky and Novoselitsky municipal districts of the Stavropol territory during the active vegetation period in 2020 based on high resolution spatial images

The article discusses the changes in the NDVI on the territory of the Alexandrovsky and Novoselitsky municipal districts of the Stavropol Territory during the active vegetation period of 2020. The data source were Sentinel-2 thematic images, the spatial resolution of which is 10 m. The schematic maps reflecting the distribution of the NDVI by dates with minimal cloud cover in June-September 2020 have been compiled and analyzed. To explain the spatial and temporal pattern of changes in the areas occupied by different gradations of the NDVI, the data of the Aleksandrovsky weather station for 2020 (the course of temperature and precipitation during the year) were analyzed here as well. The use of remote sensing data and corresponding vegetation indices allows mapping various processes and phenomena in the GIS sphere, and the compiled maps can be used to solve a wide range of theoretical and practical tasks, including crop yields assessement.

Гидроэкологическая оценка коллекторно-дренажного стока с оросительных систем Ростовской области

The purpose of the work is to conduct a comprehensive hydroecological assessment of collector-drainage waters of irrigation systems of the Rostov region in dynamics to establish spatial and temporal patterns of changes in their quality. The objects of the study were 20 main collector channels of fi ve branches of the Federal State Budgetary Institution «Management «Rostovmeliovodkhoz». Structurally, all collectors are open channels in the earth bed. For the hydrochemical assessment, an integral indicator, the coeffi cient of maximum pollution (Cmp), was used, taking into account the priority indicators of the quality of collector-drainage waters (CDW). The dynamics of the coeffi cient of water pollution limit in the reservoirs of the Semikarakorsky branch for the period 2009-2021 showed the absence of sharp fl uctuations in the quality of drainage runoff, the value of the coeffi cient of maximum pollution ranges from 0 to 1, drainage water is characterized as moderately polluted. In the collectors of the Bagaevsky branch, the value of the Cmp fl uctuates in the range of 0…2.7, drainage water is characterized as moderately polluted, polluted. The predominant ions in the drainage runoff are sulfates and sodium. The high content of sulfates in the drainage runoff is due to the introduction of sulphate groundwater, characteristic of this region. A study of collector-drainage water in the reservoirs of the Semikarakorsky and Bagaevsky branches for the content of biogenic elements in 2021 showed a regular excess of phosphates during the entire irrigation period. A comparative analysis of the quality of natural water of the CDW water intakes in the background and collector-drainage waters showed that the hydrochemical composition of the studied waters is approximately the same, which indicates the merger of water bodies and open reservoirs in the earth channel into a single hydrographic network. In 25% of the studied collectors, lower water quality was observed than in the water intake.

Hygroscopic Growth Factors of Sub-micrometer Atmospheric Aerosols at Four Selected Sites in Thailand

Hygroscopic growth factor (Gf) of aerosols is related to water loading on particles in sub-saturated conditions. It is an essential parameter for assessing the role of atmospheric particles in the radiative transfer and cloud formation process. Therefore, the temporal variation in Gf of size-selected atmospheric particles from the Aitken mode (Dp £ 100 nm) and accumulation mode (Dp > 100 nm) was measured using a humidified tandem differential mobility analyser (H-TDMA) and the relationship between particle size and Gf for various locations and meteorological conditions was determined. The origin of ambient particles primarily defines their properties and governs their participation in atmospheric processes. Thus, the measurements were performed in locations with different land-use types: urban, rural, coastal-industrial, and landlocked industrial areas. The data showed site-dependent patterns of temporal and spatial changes in Gf. The results indicated that the number-weighted Gf averaged over the investigated particle size range (30–250 nm) was highest in rural areas (Gf = 1.27), followed by coastal-industrial (Gf = 1.19), urban (Gf = 1.11), and landlocked industrial areas (Gf = 1.06). Particles in the urban and landlocked industrial areas had relatively low Gf values, suggesting that they originated mainly from fossil fuel combustion, in contrast to particles at other sites which can be attributed to coastal proximity.

Extreme precipitation changes over the Yangtze River Basin in 1901-2020

To better understand the characteristics of long-term change and variability in regional extreme precipitation and to examine possible regional responses to global climate warming, we analyzed temporal and spatial patterns of precipitation and extreme precipitation index changes in the Yangtze River Basin (YRB) over the last 120 yr. Based on the China Meteorological Administration’s daily precipitation data set of 60 city stations in mainland China from 1901-2020, we found that total annual precipitation and daily precipitation intensity of the Upper Reaches (UR) experienced a downward trend, but those of the Middle and Lower Reaches (MLR) showed an upward trend. Precipitation amount and intensity increased, while the number of precipitation days slightly decreased over the YRB during 1901-2020. Most extreme precipitation indices showed a decreasing trend in the first 60 yr that reversed after 1961 into slightly upward trends, except for consecutive dry days and wet days in the YRB during 1901-2020. There were significant downward trends for consecutive dry days and maximum 1 and 5 d precipitation during the first 60 yr, and significant upward trends for maximum 1 d precipitation, total extreme heavy precipitation, and very heavy precipitation days in the last 60 yr. The trends for extreme precipitation in the UR were lower than those in the MLR during 1901-2020, particularly in the last 60 yr, whereas the trends in the UR were higher than those in the MLR during 1901-1960. The fluctuations of extreme precipitation indices in the UR were more dramatic than those in the MLR, and the extreme precipitation indices all showed 5-10 or 20 yr periodic variabilities. The analysis also showed that observed changes in regional extreme events were directly related to rapid urbanization around the stations and multi-decadal variability of the East Asian monsoon.

SAR-TSCC: A Novel Approach for Long Time Series SAR Image Change Detection and Pattern Analysis

Change detection has played an increasingly important role in multi-temporal remote sensing applications recently. Long time series analysis is providing new information of land cover changes and improving the quality and accuracy of the change information being derived from remote sensing. The purpose of this study is to dig for more change temporal information and change pattern information from synthetic aperture radar (SAR) image time series (ITS), which is of great significance for monitoring urban area changes, conducting land-use surveys, and renovating illegal constructions. In the study, a novel unified framework for long Time Series SAR image Change detection and Change pattern mining (SAR-TSCC) was proposed for land cover change mapping. To obtain the most notable change time rapidly, a fast SAR ITS change point search method based on pruned exact linear time (SAR-PELT) algorithm was adopted. Meanwhile, the deep time series classification network, named SAR-TST, was implemented to recognize the change patterns, which is based on time series transformer (TST) architecture. Considering the lack of real training data, a novel synthetic data generation method is developed. The combination of the synthetic and real data enhanced the generalization of the classifiers. The proposed framework was used for monitoring a large urbanization area in the northwest of Hong Kong, China. The Cosmo Skymed (CSK) time series data acquired from 2013 to 2020 were exploited for land cover change analysis. Experiment results showed that our approach achieved state-of-the-art performance, as the time accuracy reached 86% and the classification accuracy on the four main change patterns (impulse, step, cycle, and complex) is over 99%. In particular, the proposed SAR-TST model showed remarkable advantages in the presence of insufficient real data.